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        of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting
        at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing
        at false origin\",\"unit\":\"metre\",\"value\":0}]},\"coordinate_system\":{\"axis\":[{\"name\":\"Easting\",\"unit\":\"metre\",\"direction\":\"east\",\"abbreviation\":\"E\"},{\"name\":\"Northing\",\"unit\":\"metre\",\"direction\":\"north\",\"abbreviation\":\"N\"}],\"subtype\":\"Cartesian\"}}},\"y\":{\"axis\":\"y\",\"step\":-1000.0,\"type\":\"spatial\",\"extent\":[-622000.0,-39000.0],\"description\":\"y
        coordinate of projection\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"ProjectedCRS\",\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"base_crs\":{\"name\":\"undefined\",\"datum\":{\"name\":\"undefined\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"id\":{\"code\":9802,\"authority\":\"EPSG\"},\"name\":\"Lambert
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        at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing
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        of the 'time_bnds' variable.\"},\"time\":{\"type\":\"temporal\",\"extent\":[\"1980-01-01T12:00:00Z\",\"2020-12-30T12:00:00Z\"],\"description\":\"24-hour
        day based on local time\"}},\"msft:group_keys\":[\"daily\",\"hawaii\"],\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\"],\"msft:storage_account\":\"daymeteuwest\",\"msft:short_description\":\"Daily
        surface weather data on a 1-km grid for Hawaii\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-seamless\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-seamless/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-seamless\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map?qt-news_science_products=0#qt-news_science_products\",\"title\":\"Public
        Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-seamless\",\"title\":\"Human
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        digital elevation data at horizontal resolutions ranging from one to sixty
        meters.\\n\\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep)
        from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map)
        are the primary elevation data product produced and distributed by the USGS.
        The 3DEP program provides raster elevation data for the conterminous United
        States, Alaska, Hawaii, and the island territories, at a variety of spatial
        resolutions.  The seamless DEM layers produced by the 3DEP program are updated
        frequently to integrate newly available, improved elevation source data.  \\n\\nDEM
        layers are available nationally at grid spacings of 1 arc-second (approximately
        30 meters) for the conterminous United States, and at approximately 1, 3,
        and 9 meters for parts of the United States. Most seamless DEM data for Alaska
        is available at a resolution of approximately 60 meters, where only lower
        resolution source data exist.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"]},\"gpkg\":{\"type\":\"application/geopackage+sqlite3\",\"roles\":[\"metadata\"]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"]},\"thumbnail\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"3dep\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"U.S.-wide
        digital elevation data at horizontal resolutions ranging from one to sixty
        meters\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-dsm\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dsm\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Digital Surface Model\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dsm-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
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        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to output a collection of Cloud Optimized GeoTIFFs, removing all points that
        have been classified as noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"COG data\",\"raster:bands\":[{\"type\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Z
        PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
        Lidar collection for the Digital Surface Model (DSM) Cloud Optimized Geotiffs
        (COGs).\",\"msft:region\":\"westeurope\"},{\"id\":\"fia\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia\"},{\"rel\":\"license\",\"href\":\"https://www.fs.usda.gov/rds/archive/datauseinfo/open\",\"type\":\"text/html\",\"title\":\"USDA
        Open Access Data Use Agreement\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/fia\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Forest
        Inventory and Analysis\",\"assets\":{\"guide\":{\"href\":\"https://www.fia.fs.fed.us/library/database-documentation/current/ver80/FIADB%20User%20Guide%20P2_8-0.pdf\",\"type\":\"application/pdf\",\"roles\":[\"metadata\"],\"title\":\"Database
        Description and User Guide\"},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fia.png\",\"type\":\"image/gif\",\"title\":\"Forest
        Inventory and Analysis\"},\"geoparquet-items\":{\"href\":\"abfs://items/fia.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[138.06,0.92,163.05,9.78],[165.28,4.57,172.03,14.61],[131.13,2.95,134.73,8.1],[-124.763068,24.523096,-66.949895,49.384358],[-179.148909,51.214183,-129.974167,71.365162],[172.461667,51.357688,179.77847,53.01075],[-178.334698,18.910361,-154.806773,28.402123],[144.618068,13.234189,144.956712,13.654383],[-67.945404,17.88328,-65.220703,18.515683],[144.886331,14.110472,146.064818,20.553802],[-65.085452,17.673976,-64.564907,18.412655],[-171.089874,-14.548699,-168.1433,-11.046934],[-178.334698,18.910361,-154.806773,28.402123]]},\"temporal\":{\"interval\":[[\"2020-06-01T00:00:00Z\",null]]}},\"license\":\"CC0-1.0\",\"keywords\":[\"Forest\",\"Species\",\"Carbon\",\"Biomass\",\"USDA\",\"Forest
        Service\"],\"providers\":[{\"url\":\"https://www.fia.fs.fed.us/\",\"name\":\"Forest
        Inventory & Analysis\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://carbonplan.org/\",\"name\":\"CarbonPlan\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"Status
        and trends on U.S. forest location, health, growth, mortality, and production,
        from the U.S. Forest Service's  [Forest Inventory and Analysis](https://www.fia.fs.fed.us/)
        (FIA) program.\\n\\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide
        survey of the forest assets of the United States. The FIA research program
        has been in existence since 1928.  FIA's primary objective is to determine
        the extent, condition, volume, growth, and use of trees on the nation's forest
        land.\\n\\nDomain: continental U.S., 1928-2018\\n\\nResolution: plot-level
        (irregular polygon)\\n\\nThis dataset was curated and brought to Azure by
        [CarbonPlan](https://carbonplan.org/).\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset
        root\",\"table:storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"Survey
        Table\",\"description\":\"Survey table. This table contains one record for
        each year an inventory is conducted in a State for annual inventory or one
        record for each periodic inventory. <br><br>* SURVEY.CN = PLOT.SRV_CN links
        the unique inventory record for a State and year to the plot records.\",\"msft:item_name\":\"survey\"},{\"name\":\"County
        Table\",\"description\":\"County table. This table contains survey unit codes
        and is also a reference table for the county codes and names. <br><br>* COUNTY.CN
        = PLOT.CTY_CN links the unique county record to the plot record.\",\"msft:item_name\":\"county\"},{\"name\":\"Plot
        Table\",\"description\":\"Plot table. This table provides information relevant
        to the entire 1-acre field plot. This table links to most other tables, and
        the linkage is made using PLOT.CN = TABLE_NAME.PLT_CN (TABLE_NAME is the name
        of any table containing the column name PLT_CN). Below are some examples of
        linking PLOT to other tables. <br><br>* PLOT.CN = COND.PLT_CN links the unique
        plot record to the condition class record(s). <br><br>* PLOT.CN = SUBPLOT.PLT_CN
        links the unique plot record to the subplot records. <br><br>* PLOT.CN = TREE.PLT_CN
        links the unique plot record to the tree records. <br><br>* PLOT.CN = SEEDLING.PLT_CN
        links the unique plot record to the seedling records.\",\"msft:item_name\":\"plot\"},{\"name\":\"Condition
        Table\",\"description\":\"Condition table. This table provides information
        on the discrete combination of landscape attributes that define the condition
        (a condition will have the same land class, reserved status, owner group,
        forest type, stand-size class, regeneration status, and stand density). <br><br>*
        PLOT.CN = COND.PLT_CN links the condition class record(s) to the plot table.
        <br><br>* COND.PLT_CN = SITETREE.PLT_CN and COND.CONDID = SITETREE.CONDID
        links the condition class record to the site tree data. <br><br>* COND.PLT_CN
        = TREE.PLT_CN and COND.CONDID = TREE.CONDID links the condition class record
        to the tree data.\",\"msft:item_name\":\"cond\"},{\"name\":\"Subplot Table\",\"description\":\"Subplot
        table. This table describes the features of a single subplot. There are multiple
        subplots per 1-acre field plot and there can be multiple conditions sampled
        on each subplot. <br><br>* PLOT.CN = SUBPLOT.PLT_CN links the unique plot
        record to the subplot records. <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and
        SUBPLOT.MACRCOND = COND.CONDID links the macroplot conditions to the condition
        class record. <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.SUBPCOND
        = COND.CONDID links the subplot conditions to the condition class record.
        <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MICRCOND = COND.CONDID
        links the microplot conditions to the condition class record.\",\"msft:item_name\":\"subplot\"},{\"name\":\"Subplot
        Condition Table\",\"description\":\"Subplot condition table. This table contains
        information about the proportion of a subplot in a condition. <br><br>* PLOT.CN
        = SUBP_COND.PLT_CN links the subplot condition class record to the plot table.
        <br><br>* SUBP_COND.PLT_CN = COND.PLT_CN and SUBP_COND.CONDID = COND.CONDID
        links the condition class records found on the four subplots to the subplot
        description.\",\"msft:item_name\":\"subp_cond\"},{\"name\":\"boundary\",\"description\":\"Boundary
        table. This table provides a description of the demarcation line between two
        conditions that occur on a single subplot\",\"msft:item_name\":\"boundary\"},{\"name\":\"Subplot
        Condition Change Matrix\",\"description\":\"Subplot condition change matrix
        table. This table contains information about the mix of current and previous
        conditions that occupy the same area on the subplot. <br><br>* PLOT.CN = SUBP_COND_CHNG_MTRX.PLT_CN
        links the subplot condition change matrix records to the unique plot record.
        <br><br>* PLOT.PREV_PLT_CN = SUBP_COND_CHNG_MTRX.PREV_PLT_CN links the subplot
        condition change matrix records to the unique previous plot record.\",\"msft:item_name\":\"subp_cond_chng_mtrx\"},{\"name\":\"Tree
        Table\",\"description\":\"Tree table. This table provides information for
        each tree 1 inch in diameter and larger found on a microplot, subplot, or
        core optional macroplot. <br><br>* PLOT.CN = TREE.PLT_CN links the tree records
        to the unique plot record. <br><br>* COND.PLT_CN = TREE.PLT_CN and COND.CONDID
        = TREE.CONDID links the tree records to the unique condition record.\",\"msft:item_name\":\"tree\"},{\"name\":\"Tree
        Woodland Stems Table\",\"description\":\"Tree woodland stems table. This table
        stores data for the individual stems of a woodland species tree. Individual
        woodland stem diameter measurements contribute to the calculation of the diameter
        stored on the parent TREE table record. <br><br>* TREE.CN = TREE_WOODLAND_STEMS.TRE_CN
        links a woodland stems record to the corresponding unique tree record.\",\"msft:item_name\":\"tree_woodland_stems\"},{\"name\":\"Tree
        Regional Biomass Table\",\"description\":\"Tree regional biomass table. This
        table contains biomass estimates computed using equations and methodology
        that varies by FIA work unit. This table retains valuable information for
        generating biomass estimates that match earlier published reports. <br><br>*
        TREE.CN = TREE_REGIONAL_BIOMASS.TRE_CN links a tree regional biomass record
        to the corresponding unique tree.\",\"msft:item_name\":\"tree_regional_biomass\"},{\"name\":\"Tree
        Net Growth, Removal, and Mortality Component Table\",\"description\":\"Tree
        net growth, removal, and mortality component table. This table stores information
        used to compute net growth, removals, and mortality estimates for remeasurement
        trees. Each remeasurement tree has a single record in this table. <br><br>*
        TREE_GRM_COMPONENT.TRE_CN = TREE.TRE_CN links the records in this table to
        the corresponding tree record in the TREE table.\",\"msft:item_name\":\"tree_grm_component\"},{\"name\":\"Tree
        Net Growth, Removal, and Mortality Midpoint Table\",\"description\":\"Tree
        net growth, removal, and mortality midpoint table. This table contains information
        about a remeasured tree at the midpoint of the remeasurement period. It does
        not contain a record for every tree. Midpoint estimates are computed for trees
        that experience mortality, removal, or land use diversion or reversion. The
        information in this table is used to compute net growth, removal, and mortality
        estimates on remeasurement trees. <br><br>* TREE_GRM_MIDPT.TRE_CN = TREE.TRE_CN
        links the records in this table to the corresponding tree record in the TREE
        table.\",\"msft:item_name\":\"tree_grm_midpt\"},{\"name\":\"Tree Net Growth,
        Removal, and Mortality Begin Table\",\"description\":\"Tree net growth, removal,
        and mortality begin table. This table contains information for remeasured
        trees where values have been calculated for the beginning of the remeasurement
        period. Only those trees where information was recalculated for time 1 (T1)
        are included. The information in this table is used to produce net growth,
        removal and mortality estimates on remeasured trees. <br><br>* TREE_GRM_BEGIN.TRE_CN
        = TREE.TRE_CN links the records in this table to the corresponding tree record
        in the TREE table.\",\"msft:item_name\":\"tree_grm_begin\"},{\"name\":\"Tree
        Net Growth, Removal, and Mortality Estimation Table\",\"description\":\"Tree
        net growth, removal, and mortality estimation table. This table contains information
        used to produce estimates of growth, removals and mortality. <br><br>* PLOT.CN
        = TREE_GRM_ESTN.PLT_CN links the tree GRM estimation records to the unique
        plot record. <br><br>* TREE.CN = TREE_GRM_ESTN.TRE_CN links the tree GRM estimation
        records to the unique tree record.\",\"msft:item_name\":\"tree_grm_estn\"},{\"name\":\"Seedling
        Table\",\"description\":\"Seedling table. This table provides a count of the
        number of live trees of a species found on a microplot that are less than
        1 inch in diameter but at least 6 inches in length for conifer species or
        at least 12 inches in length for hardwood species. <br><br>* PLOT.CN = SEEDLING.PLT_CN
        links the seedling records to the unique plot record. <br><br>* COND.PLT_CN
        = SEEDLING.PLT_CN and COND.CONDID = SEEDLING.CONDID links the condition record
        to the seedling record.\",\"msft:item_name\":\"seedling\"},{\"name\":\"Site
        Tree Table\",\"description\":\"Site tree table. This table provides information
        on the site tree(s) collected in order to calculate site index and/or site
        productivity information for a condition. <br><br>* PLOT.CN = SITETREE.PLT_CN
        links the site tree records to the unique plot record. <br><br>* SITETREE.PLT_CN
        = COND.PLT_CN and SITETREE.CONDID = COND.CONDID links the site tree record(s)
        to the unique condition class record.\",\"msft:item_name\":\"sitetree\"},{\"name\":\"Invasive
        Subplot Species Table\",\"description\":\"Invasive subplot species table.
        This table provides percent cover data of invasive species identified on the
        subplot. <br><br>* PLOT.CN = INVASIVE_SUBPLOT_SPP.PLT_CN links the invasive
        subplot species record(s) to the unique plot record. <br><br>* SUBP_COND.PLT_CN
        = INVASIVE_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = INVASIVE_SUBPLOT_SPP.CONDID
        and SUBP_COND.SUBP = INVASIVE_SUBPLOT_SPP.SUBP links the invasive subplot
        species record(s) to the unique subplot condition record. <br><br>* INVASIVE_SUBPLOT_SPP.VEG_SPCD
        = REF_PLANT_DICTIONARY.SYMBOL links the invasive vegetation subplot NRCS species
        code to the plant dictionary reference species code.\",\"msft:item_name\":\"invasive_subplot_spp\"},{\"name\":\"Phase
        2 Vegetation Subplot Species Table\",\"description\":\"Phase 2 Vegetation
        subplot species table. This table provides percent cover data of vegetation
        species identified on the subplot. <br><br>* PLOT.CN = P2VEG_SUBPLOT_SPP.PLT_CN
        links the vegetation subplot species record(s) to the unique plot record.
        <br><br>* SUBP_COND.PLT_CN = P2VEG_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID
        = P2VEG_SUBPLOT_SPP.CONDID and SUBP_COND.SUBP = P2VEG_SUBPLOT_SPP.SUBP links
        the vegetation subplot species record(s) to the unique subplot condition record.
        <br><br>* P2VEG_SUBPLOT_SPP.VEG_SPCD = REF_PLANT_DICTIONARY.SYMBOL links the
        P2 vegetation subplot NRCS species code to the plant dictionary reference
        species code.\",\"msft:item_name\":\"p2veg_subplot_spp\"},{\"name\":\"Phase
        2 Vegetation Subplot Structure Table\",\"description\":\"Phase 2 Vegetation
        subplot structure table. This table provides percent cover by layer by growth
        habit. <br><br>* PLOT.CN = P2VEG_SUBP_STRUCTURE. PLT_CN links the subplot
        structure record(s) to the unique plot record. <br><br>* SUBP_COND.PLT_CN
        = P2VEG_SUBP_STRUCTURE.PLT_CN and SUBP_COND.CONDID = P2VEG_SUBP_STRUCTURE.CONDID
        and SUBP_COND.SUBP = P2VEG_SUBP_STRUCTURE.SUBP links the vegetation subplot
        structure record(s) to the unique subplot condition record.\",\"msft:item_name\":\"p2veg_subp_structure\"},{\"name\":\"Down
        Woody Material Visit Table\",\"description\":\"Down woody material visit table.
        This table provides general information on down woody material indicator visit,
        such as the date of the DWM survey. <br><br>* PLOT.CN = DWM_VISIT.PLT_CN links
        the down woody material indicator visit record to the unique plot record.\",\"msft:item_name\":\"dwm_visit\"},{\"name\":\"Down
        Woody Material Coarse Woody Debris Table\",\"description\":\"Down woody material
        coarse woody debris table. This table provides information for each piece
        of coarse woody debris measured along the transects. <br><br>* PLOT.CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN
        links the down woody material coarse woody debris records to the unique plot
        record. <br><br>* COND.PLT_CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN and COND.CONDID=
        DWM_COARSE_WOODY_DEBRIS.CONDID links the coarse woody debris records to the
        unique condition record.\",\"msft:item_name\":\"dwm_coarse_woody_debris\"},{\"name\":\"Down
        Woody Material Duff, Litter, Fuel Table\",\"description\":\"Down woody material
        duff, litter, fuel table. This table provides information on the duff, litter,
        fuelbed depths measured at a point on the transects. <br><br>* PLOT.CN = DWM_DUFF_LITTER_FUEL.PLT_CN
        links the duff, litter, fuelbed records to the unique plot record. <br><br>*
        COND.PLT_CN = DWM_DUFF_LITTER_FUEL.PLT_CN and COND.CONDID= DWM_DUFF_LITTER_FUEL.CONDID
        links the duff, litter, fuel records to the unique condition record.\",\"msft:item_name\":\"dwm_duff_litter_fuel\"},{\"name\":\"Down
        Woody Material Fine Woody Debris Table\",\"description\":\"Down woody material
        fine woody debris table. This table provides information on the fine woody
        debris measured along a segment of the transects. <br><br>* PLOT.CN = DWM_FINE_WOODY_DEBRIS.PLT_CN
        links the fine woody debris records to the unique plot record. <br><br>* COND.PLT_CN
        = DWM_FINE_WOODY_DEBRIS.PLT_CN and COND.CONDID= DWM_FINE_WOODY_DEBRIS.CONDID
        links the fine woody debris records to the unique condition record.\",\"msft:item_name\":\"dwm_fine_woody_debris\"},{\"name\":\"Down
        Woody Material Microplot Fuel Table\",\"description\":\"Down woody material
        microplot fuel table. This table provides information on the fuel loads (shrubs
        and herbs) measured on the microplot. <br><br>* PLOT.CN = DWM_MICROPLOT_FUEL.PLT_CN
        links the microplot fuel records to the unique plot record.\",\"msft:item_name\":\"dwm_microplot_fuel\"},{\"name\":\"Down
        Woody Material Residual Pile Table\",\"description\":\"Down woody material
        residual pile table. This table provides information on the wood piles measured
        on the subplot. <br><br>* PLOT.CN = DWM_RESIDUAL_PILE.PLT_CN links the wood
        piles records to the unique plot record. <br><br>* COND.PLT_CN = DWM_RESIDUAL_PILE.PLT_CN
        and COND.CONDID= DWM_RESIDUAL_PILE.CONDID links the wood piles records to
        the unique condition record.\",\"msft:item_name\":\"dwm_residual_pile\"},{\"name\":\"Down
        Woody Material Transect Segment Table\",\"description\":\"Down woody material
        transect segment table. This table describes the down woody material transect
        segment lengths by condition class. <br><br>* PLOT.CN = DWM_TRANSECT_SEGMENT.PLT_CN
        links the down woody material transect length records to the unique plot record.
        <br><br>* COND.PLT_CN = DWM_TRANSECT_SEGMENT.PLT_CN and COND.CONDID= DWM_TRANSECT_SEGMENT.CONDID
        links the down woody material transect segment records to the unique condition
        record.\",\"msft:item_name\":\"dwm_transect_segment\"},{\"name\":\"Condition
        Down Woody Material Calculation Table\",\"description\":\"Condition down woody
        material calculation table. This table contains calculated values and condition-level
        estimates for down woody attributes by plot number (PLOT), condition class
        number (CONDID), and evaluation identifier (EVALID). <br><br>* PLOT.CN = COND_DWM_CALC.PLT_CN
        links the down woody material calculation records to the unique plot record.
        <br><br>* COND.CN = COND_DWM_CALC.CND_CN links the down woody material calculation
        records to the unique condition record. <br><br>* POP_STRATUM. CN = COND_DWM_CALC.STRATUM_CN
        links the down woody material calculation records to the unique population
        stratum record.\",\"msft:item_name\":\"cond_dwm_calc\"},{\"name\":\"Plot Regeneration
        Table\",\"description\":\"Plot regeneration table. This table contains the
        information for the four subplots describing the amount of animal browse pressure
        exerted on the regeneration of trees. <br><br>* PLOT.CN = PLOT_REGEN.PLT_CN
        links the unique plot record to the unique plot regeneration record.\",\"msft:item_name\":\"plot_regen\"},{\"name\":\"Subplot
        Regeneration Table\",\"description\":\"Subplot regeneration table. This table
        provides information on the subplot survey status and the site survey limitations,
        if any, for the tree regeneration study. <br><br>* PLOT.CN = SUBPLOT_REGEN.PLT_CN
        links the unique plot record to the subplot regeneration records. <br><br>*
        SUBPLOT.PLT_CN = SUBPLOT_REGEN.PLT_CN and SUBPLOT.SUBP = SUBPLOT_REGEN.SUBP
        links the subplot record to the subplot regeneration record.\",\"msft:item_name\":\"subplot_regen\"},{\"name\":\"Seedling
        Regeneration Table\",\"description\":\"Seedling regeneration. This table contains
        provides information on the seedling count by condition, species, source,
        and length class for the tree regeneration study. <br><br>* PLOT.CN = SEEDLING_REGEN.PLT_CN
        links the unique plot record to the seedling regeneration records. <br><br>*
        COND.PLT_CN = SEEDLING_REGEN.PLT_CN and COND.CONDID = SEEDLING_REGEN.CONDID
        links the regeneration seedling records to the unique condition record.\",\"msft:item_name\":\"seedling_regen\"},{\"name\":\"Population
        Estimation Unit Table\",\"description\":\"Population estimation unit table.
        This table contains information about estimation units. An estimation unit
        is a geographic area that can be drawn on a map. It has a known area, and
        the sampling intensity must be the same within a stratum within an estimation
        unit. Generally, estimation units are contiguous areas, but exceptions are
        made when certain ownerships, usually National Forests, are sampled at different
        intensities. One record in the POP_ESTN_UNIT table corresponds to a single
        estimation unit. POP_ESTN_UNIT.CN = POP_STRATUM.ESTN_UNIT_CN links the unique
        stratified geographical area (ESTN_UNIT) to the strata (STRATUMCD) that are
        assigned to each ESTN_UNIT.\",\"msft:item_name\":\"pop_estn_unit\"},{\"name\":\"Population
        Evaluation Table\",\"description\":\"Population evaluation table. This table
        provides information about evaluations. An evaluation is the combination of
        a set of plots (the sample) and a set of Phase 1 data (obtained through remote
        sensing, called a stratification) that can be used to produce population estimates
        for a State (an evaluation may be created to produce population estimates
        for a region other than a State, such as the Black Hills National Forest).
        A record in the POP_EVAL table identifies one evaluation and provides some
        descriptive information about how the evaluation may be used. <br><br>* POP_ESTN_UNIT.EVAL_CN
        = POP_EVAL.CN links the unique evaluation identifier (EVALID) in the POP_EVAL
        table to the unique geographical areas (ESTN_UNIT) that are stratified. Within
        a population evaluation (EVALID) there can be multiple population estimation
        units, or geographic areas across which there are a number of values being
        estimated (e.g., estimation of volume across counties for a given State).\",\"msft:item_name\":\"pop_eval\"},{\"name\":\"Population
        Evaluation Attribute Table\",\"description\":\"Population evaluation attribute
        table. This table provides information as to which population estimates can
        be provided by an evaluation. If an evaluation can produce only 22 of all
        the population estimates in the REF_POP_ATTRIBUTE table, there will be 22
        records in the POP_EVAL_ATTRIBUTE table (one per population estimate) for
        that evaluation. <br><br>* POP_EVAL.CN = POP_EVAL_ATTRIBUTE.EVAL_CN links
        the unique evaluation identifier to the list of population estimates that
        can be derived for that evaluation.\",\"msft:item_name\":\"pop_eval_attribute\"},{\"name\":\"Population
        Evaluation Group Table\",\"description\":\"Population evaluation group table.
        This table lists and describes the evaluation groups. One record in the POP_EVAL_GRP
        table can be linked to all the evaluations that were used in generating estimates
        for a State inventory report. <br><br>* POP_EVAL_GRP.CN = POP_EVAL_TYP.EVAL_GRP_CN
        links the evaluation group record to the evaluation type record.\",\"msft:item_name\":\"pop_eval_grp\"},{\"name\":\"Population
        Evaluation Type Table\",\"description\":\"Population evaluation type table.
        This table provides information on the type of evaluations that were used
        to generate a set of tables for an inventory report. In a typical State inventory
        report, one evaluation is used to generate an estimate of the total land area;
        a second evaluation is used to generate current estimates of volume, numbers
        of trees and biomass; and a third evaluation is used for estimating growth,
        removals and mortality. <br><br>* POP_EVAL_TYP.EVAL_CN = POP_EVAL.CN links
        the evaluation type record to the evaluation record. <br><br>* POP_EVAL_TYP.EVAL_GRP_CN
        = POP_EVAL_GRP.CN links the evaluation type record to the evaluation group
        record. <br><br>* POP_EVAL_TYP.EVAL_TYP = REF_POP_EVAL_TYP_DESCR.EVAL_TYP
        links an evaluation type record to an evaluation type description reference
        record.\",\"msft:item_name\":\"pop_eval_typ\"},{\"name\":\"Population Plot
        Stratum Assignment Table\",\"description\":\"Population plot stratum assignment
        table. This table provides a way to assign stratum information to a plot.
        Stratum information is assigned to a plot by overlaying the plot's location
        on the Phase 1 imagery. Plots are linked to their appropriate stratum for
        an evaluation via the POP_PLOT_STRATUM_ASSGN table. <br><br>* POP_PLOT_STRATUM_ASSGN.PLT_CN
        = PLOT.CN links the stratum assigned to the plot record.\",\"msft:item_name\":\"pop_plot_stratum_assgn\"},{\"name\":\"Population
        Stratum Table\",\"description\":\"Population stratum table. This table provides
        information about individual strata. The area within an estimation unit is
        divided into strata. The area for each stratum can be calculated by determining
        the proportion of Phase 1 pixels/plots in each stratum and multiplying that
        proportion by the total area in the estimation unit. Information for a single
        stratum is stored in a single record of the POP_STRATUM table. <br><br>* POP_STRATUM.CN
        = POP_PLOT_STRATUM_ASSGN.STRATUM_CN links the defined stratum to each plot.\",\"msft:item_name\":\"pop_stratum\"},{\"name\":\"Plot
        Geometry Table\",\"description\":\"Plot geometry table. This table contains
        geometric attributes associated with the plot location, such as the hydrological
        unit and roadless codes. <br><br>* PLOTGEOM.CN = PLOT.CN links the unique
        plot record between the two tables.\",\"msft:item_name\":\"plotgeom\"},{\"name\":\"Plot
        Snapshot Table\",\"description\":\"Plot snapshot table. This table combines
        the information in the PLOT table with information in the PLOT_EVAL_GRP and
        POP_STRATUM tables to provide a snapshot of the plot records with their associated
        expansion and adjustment factors. <br><br>* PLOTSNAP.CN = PLOT.CN links the
        unique plot record between the two tables.\",\"msft:item_name\":\"plotsnap\"}],\"msft:container\":\"cpdata\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"cpdataeuwest\",\"msft:short_description\":\"Status
        and trends on U.S. forest location, health, growth, mortality, and production,
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        thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://gridmet/gridmet.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\"],\"description\":\"Azure
        Blob File System URI of the gridMET Zarr Group on Azure Blob Storage for use
        with adlfs.\",\"xarray:open_kwargs\":{\"consolidated\":true},\"xarray:storage_options\":{\"account_name\":\"ai4edataeuwest\"}},\"zarr-https\":{\"href\":\"https://ai4edataeuwest.blob.core.windows.net/gridmet/gridmet.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"https\"],\"title\":\"gridMET
        HTTPS Zarr root\",\"description\":\"HTTPS URI of the gridMET Zarr Group on
        Azure Blob Storage.\",\"xarray:open_kwargs\":{\"consolidated\":true}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.76666663333334,25.066666666666666,-67.05833330000002,49.400000000000006]]},\"temporal\":{\"interval\":[[\"1979-01-01T00:00:00Z\",\"2020-12-31T00:00:00Z\"]]}},\"license\":\"CC0-1.0\",\"keywords\":[\"gridMET\",\"Water\",\"Precipitation\",\"Temperature\",\"Vapor
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        daily surface meteorological data at approximately four-kilometer resolution,
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        lat time\",\"description\":\"Daily Mean Wind Speed\",\"grid_mapping\":\"crs\",\"standard_name\":\"vs\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Mean Wind Speed\"},\"air_temperature\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"long_name\":\"tmmn\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily Minimum Temperature\",\"grid_mapping\":\"crs\",\"standard_name\":\"tmmn\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Minimum Temperature\"},\"burning_index_g\":{\"type\":\"data\",\"unit\":\"Unitless\",\"attrs\":{\"units\":\"Unitless\",\"long_name\":\"bi\",\"dimensions\":\"lon
        lat time\",\"description\":\"BI-G\",\"grid_mapping\":\"crs\",\"standard_name\":\"bi\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"BI-G\"},\"relative_humidity\":{\"type\":\"data\",\"unit\":\"%\",\"attrs\":{\"units\":\"%\",\"long_name\":\"rmin\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily Minimum Relative Humidity\",\"grid_mapping\":\"crs\",\"standard_name\":\"rmin\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Minimum Relative Humidity\"},\"specific_humidity\":{\"type\":\"data\",\"unit\":\"kg/kg\",\"attrs\":{\"units\":\"kg/kg\",\"long_name\":\"sph\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily mean specific humidity\",\"grid_mapping\":\"crs\",\"standard_name\":\"sph\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        mean specific humidity\"},\"wind_from_direction\":{\"type\":\"data\",\"unit\":\"Degrees
        Clockwise from north\",\"attrs\":{\"units\":\"Degrees Clockwise from north\",\"long_name\":\"th\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily mean wind direction\",\"grid_mapping\":\"crs\",\"standard_name\":\"th\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        mean wind direction\"},\"precipitation_amount\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"long_name\":\"pr\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily Accumulated Precipitation\",\"grid_mapping\":\"crs\",\"standard_name\":\"pr\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Accumulated Precipitation\"},\"dead_fuel_moisture_100hr\":{\"type\":\"data\",\"unit\":\"Percent\",\"attrs\":{\"units\":\"Percent\",\"long_name\":\"fm100\",\"dimensions\":\"lon
        lat time\",\"description\":\"100 hour fuel moisture\",\"grid_mapping\":\"crs\",\"standard_name\":\"fm100\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"100
        hour fuel moisture\"},\"dead_fuel_moisture_1000hr\":{\"type\":\"data\",\"unit\":\"Percent\",\"attrs\":{\"units\":\"Percent\",\"long_name\":\"fm1000\",\"dimensions\":\"lon
        lat time\",\"description\":\"1000 hour fuel moisture\",\"grid_mapping\":\"crs\",\"standard_name\":\"fm1000\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"1000
        hour fuel moisture\"},\"mean_vapor_pressure_deficit\":{\"type\":\"data\",\"unit\":\"kPa\",\"attrs\":{\"units\":\"kPa\",\"long_name\":\"vpd\",\"dimensions\":\"lon
        lat time\",\"description\":\"mean vapor presure deficit\",\"grid_mapping\":\"crs\",\"standard_name\":\"vpd\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"mean
        vapor presure deficit\"},\"potential_evapotranspiration\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"long_name\":\"pet\",\"dimensions\":\"lon
        lat time\",\"description\":\"Daily reference evapotranspiration (short grass)\",\"grid_mapping\":\"crs\",\"standard_name\":\"pet\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[15341,585,1386],\"chunks\":[30,585,1386],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        reference evapotranspiration (short grass)\"},\"surface_downwelling_shortwave_flux_in_air\":{\"type\":\"data\",\"unit\":\"W
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        at false origin\",\"unit\":\"metre\",\"value\":0}]},\"coordinate_system\":{\"axis\":[{\"name\":\"Easting\",\"unit\":\"metre\",\"direction\":\"east\",\"abbreviation\":\"E\"},{\"name\":\"Northing\",\"unit\":\"metre\",\"direction\":\"north\",\"abbreviation\":\"N\"}],\"subtype\":\"Cartesian\"}}},\"y\":{\"axis\":\"y\",\"step\":-1000.0,\"type\":\"spatial\",\"extent\":[-622000.0,-39000.0],\"description\":\"y
        coordinate of projection\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"ProjectedCRS\",\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"base_crs\":{\"name\":\"undefined\",\"datum\":{\"name\":\"undefined\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"id\":{\"code\":9802,\"authority\":\"EPSG\"},\"name\":\"Lambert
        Conic Conformal (2SP)\"},\"parameters\":[{\"id\":{\"code\":8823,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of 1st standard parallel\",\"unit\":\"degree\",\"value\":25},{\"id\":{\"code\":8824,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of 2nd standard parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude
        of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting
        at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing
        at false origin\",\"unit\":\"metre\",\"value\":0}]},\"coordinate_system\":{\"axis\":[{\"name\":\"Easting\",\"unit\":\"metre\",\"direction\":\"east\",\"abbreviation\":\"E\"},{\"name\":\"Northing\",\"unit\":\"metre\",\"direction\":\"north\",\"abbreviation\":\"N\"}],\"subtype\":\"Cartesian\"}}},\"nv\":{\"type\":\"count\",\"values\":[0,1],\"description\":\"Size
        of the 'time_bnds' variable.\"},\"time\":{\"type\":\"temporal\",\"extent\":[\"1980-01-16T12:00:00Z\",\"2020-12-16T00:00:00Z\"],\"description\":\"24-hour
        day based on local time\"}},\"msft:group_keys\":[\"monthly\",\"hawaii\"],\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\"],\"msft:storage_account\":\"daymeteuwest\",\"msft:short_description\":\"Monthly
        climate summaries on a 1-km grid for Hawaii\",\"msft:region\":\"westeurope\"},{\"id\":\"daymet-monthly-pr\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-monthly-pr/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-monthly-pr\"},{\"rel\":\"license\",\"href\":\"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\",\"title\":\"EOSDIS
        Data Use Policy\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.3334/ORNLDAAC/1855\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/daymet-monthly-pr\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Daymet
        Monthly Puerto Rico\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/daymet-monthly-pr.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Daymet
        monthly Puerto Rico map thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://daymet-zarr/monthly/pr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"abfs\"],\"title\":\"Monthly
        Puerto Rico Daymet Azure Blob File System Zarr root\",\"description\":\"Azure
        Blob File System of the monthly Puerto Rico Daymet Zarr Group on Azure Blob
        Storage for use with adlfs.\",\"xarray:open_kwargs\":{\"consolidated\":true},\"xarray:storage_options\":{\"account_name\":\"daymeteuwest\"}},\"zarr-https\":{\"href\":\"https://daymeteuwest.blob.core.windows.net/daymet-zarr/monthly/pr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"https\"],\"title\":\"Monthly
        Puerto Rico Daymet HTTPS Zarr root\",\"description\":\"HTTPS URI of the monthly
        Puerto Rico Daymet Zarr Group on Azure Blob Storage.\",\"xarray:open_kwargs\":{\"consolidated\":true}}},\"extent\":{\"spatial\":{\"bbox\":[[-67.9927,16.8444,-64.1196,19.9382]]},\"temporal\":{\"interval\":[[\"1980-01-16T12:00:00Z\",\"2020-12-16T00:00:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.3334/ORNLDAAC/1855\",\"keywords\":[\"Daymet\",\"Puerto
        Rico\",\"Temperature\",\"Precipitation\",\"Vapor Pressure\",\"Climate\"],\"providers\":[{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"https://doi.org/10.3334/ORNLDAAC/1855\",\"name\":\"ORNL
        DAAC\",\"roles\":[\"producer\"]}],\"description\":\"Monthly climate summaries
        derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1
        km x 1 km spatial resolution for five variables: minimum and maximum temperature,
        precipitation, vapor pressure, and snow water equivalent. Annual averages
        are provided for minimum and maximum temperature, vapor pressure, and snow
        water equivalent, and annual totals are provided for the precipitation variable.\\n\\n[Daymet](https://daymet.ornl.gov/)
        provides measurements of near-surface meteorological conditions; the main
        purpose is to provide data estimates where no instrumentation exists. The
        dataset covers the period from January 1, 1980 to the present. Each year is
        processed individually at the close of a calendar year. Data are in a Lambert
        conformal conic projection for North America and are distributed in Zarr and
        NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions
        (version 1.6)](http://cfconventions.org/).\\n\\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855)
        to cite your usage of the data.\\n\\nThis dataset provides coverage for Hawaii;
        North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\\n\",\"sci:citation\":\"Thornton,
        M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet:
        Monthly Climate Summaries on a 1-km Grid for North America, Version 4. ORNL
        DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1855\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"daymet\",\"cube:variables\":{\"vp\":{\"type\":\"data\",\"unit\":\"Pa\",\"attrs\":{\"units\":\"Pa\",\"long_name\":\"monthly
        average of daily average vapor pressure\",\"cell_methods\":\"area: mean time:
        mean within days time: mean over days\",\"grid_mapping\":\"lambert_conformal_conic\"},\"shape\":[492,231,364],\"chunks\":[12,231,364],\"dimensions\":[\"time\",\"y\",\"x\"],\"description\":\"monthly
        average of daily average vapor pressure\"},\"lat\":{\"type\":\"auxiliary\",\"unit\":\"degrees_north\",\"attrs\":{\"units\":\"degrees_north\",\"long_name\":\"latitude
        coordinate\",\"standard_name\":\"latitude\"},\"shape\":[231,364],\"chunks\":[231,364],\"dimensions\":[\"y\",\"x\"],\"description\":\"latitude
        coordinate\"},\"lon\":{\"type\":\"auxiliary\",\"unit\":\"degrees_east\",\"attrs\":{\"units\":\"degrees_east\",\"long_name\":\"longitude
        coordinate\",\"standard_name\":\"longitude\"},\"shape\":[231,364],\"chunks\":[231,364],\"dimensions\":[\"y\",\"x\"],\"description\":\"longitude
        coordinate\"},\"swe\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"long_name\":\"monthly
        total snow water equivalent\",\"cell_methods\":\"area: mean time: sum within
        days time: sum over days\",\"grid_mapping\":\"lambert_conformal_conic\"},\"shape\":[492,231,364],\"chunks\":[12,231,364],\"dimensions\":[\"time\",\"y\",\"x\"],\"description\":\"monthly
        total snow water equivalent\"},\"prcp\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"long_name\":\"monthly
        total precipitation\",\"cell_methods\":\"area: mean time: sum within days
        time: sum over days\",\"grid_mapping\":\"lambert_conformal_conic\"},\"shape\":[492,231,364],\"chunks\":[12,231,364],\"dimensions\":[\"time\",\"y\",\"x\"],\"description\":\"monthly
        total precipitation\"},\"tmax\":{\"type\":\"data\",\"unit\":\"degrees C\",\"attrs\":{\"units\":\"degrees
        C\",\"long_name\":\"monthly average of daily maximum temperature\",\"cell_methods\":\"area:
        mean time: maximum within days time: mean over days\",\"grid_mapping\":\"lambert_conformal_conic\"},\"shape\":[492,231,364],\"chunks\":[12,231,364],\"dimensions\":[\"time\",\"y\",\"x\"],\"description\":\"monthly
        average of daily maximum temperature\"},\"tmin\":{\"type\":\"data\",\"unit\":\"degrees
        C\",\"attrs\":{\"units\":\"degrees C\",\"long_name\":\"monthly average of
        daily minimum temperature\",\"cell_methods\":\"area: mean time: minimum within
        days time: mean over days\",\"grid_mapping\":\"lambert_conformal_conic\"},\"shape\":[492,231,364],\"chunks\":[12,231,364],\"dimensions\":[\"time\",\"y\",\"x\"],\"description\":\"monthly
        average of daily minimum temperature\"},\"time_bnds\":{\"type\":\"data\",\"attrs\":{},\"shape\":[492,2],\"chunks\":[12,2],\"dimensions\":[\"time\",\"nv\"]},\"lambert_conformal_conic\":{\"type\":\"data\",\"attrs\":{\"false_easting\":0.0,\"false_northing\":0.0,\"semi_major_axis\":6378137.0,\"grid_mapping_name\":\"lambert_conformal_conic\",\"standard_parallel\":[25.0,60.0],\"inverse_flattening\":298.257223563,\"latitude_of_projection_origin\":42.5,\"longitude_of_central_meridian\":-100.0},\"shape\":[],\"dimensions\":[]}},\"msft:container\":\"daymet-zarr\",\"cube:dimensions\":{\"x\":{\"axis\":\"x\",\"step\":1000.0,\"type\":\"spatial\",\"extent\":[3445750.0,3808750.0],\"description\":\"x
        coordinate of projection\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"ProjectedCRS\",\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"base_crs\":{\"name\":\"undefined\",\"datum\":{\"name\":\"undefined\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"id\":{\"code\":9802,\"authority\":\"EPSG\"},\"name\":\"Lambert
        Conic Conformal (2SP)\"},\"parameters\":[{\"id\":{\"code\":8823,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of 1st standard parallel\",\"unit\":\"degree\",\"value\":25},{\"id\":{\"code\":8824,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of 2nd standard parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude
        of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting
        at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing
        at false origin\",\"unit\":\"metre\",\"value\":0}]},\"coordinate_system\":{\"axis\":[{\"name\":\"Easting\",\"unit\":\"metre\",\"direction\":\"east\",\"abbreviation\":\"E\"},{\"name\":\"Northing\",\"unit\":\"metre\",\"direction\":\"north\",\"abbreviation\":\"N\"}],\"subtype\":\"Cartesian\"}}},\"y\":{\"axis\":\"y\",\"step\":-1000.0,\"type\":\"spatial\",\"extent\":[-1995000.0,-1765000.0],\"description\":\"y
        coordinate of projection\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"ProjectedCRS\",\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"base_crs\":{\"name\":\"undefined\",\"datum\":{\"name\":\"undefined\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"id\":{\"code\":9802,\"authority\":\"EPSG\"},\"name\":\"Lambert
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        of 1st standard parallel\",\"unit\":\"degree\",\"value\":25},{\"id\":{\"code\":8824,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of 2nd standard parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude
        of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting
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        at false origin\",\"unit\":\"metre\",\"value\":0}]},\"coordinate_system\":{\"axis\":[{\"name\":\"Easting\",\"unit\":\"metre\",\"direction\":\"east\",\"abbreviation\":\"E\"},{\"name\":\"Northing\",\"unit\":\"metre\",\"direction\":\"north\",\"abbreviation\":\"N\"}],\"subtype\":\"Cartesian\"}}},\"nv\":{\"type\":\"count\",\"values\":[0,1],\"description\":\"Size
        of the 'time_bnds' variable.\"},\"time\":{\"type\":\"temporal\",\"extent\":[\"1980-01-16T12:00:00Z\",\"2020-12-16T00:00:00Z\"],\"description\":\"24-hour
        day based on local time\"}},\"msft:group_keys\":[\"monthly\",\"puerto rico\"],\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\"],\"msft:storage_account\":\"daymeteuwest\",\"msft:short_description\":\"Monthly
        climate summaries on a 1-km grid for Puerto Rico\",\"msft:region\":\"westeurope\"},{\"id\":\"gnatsgo-tables\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-tables/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-tables\"},{\"rel\":\"about\",\"href\":\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf\",\"type\":\"application/pdf\",\"title\":\"gSSURGO
        User Guide\",\"description\":\"User guide for gSSURGO\"},{\"rel\":\"about\",\"href\":\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1464658&ext=pdf\",\"type\":\"application/pdf\",\"title\":\"gNATSGO
        Overview Slides\",\"description\":\"Slides giving a high level overview of
        the gNATSGO dataset\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/\",\"title\":\"CC0
        1.0 Universal Public Domain Dedication\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"gNATSGO
        Soil Database - Tables\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-tables.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"gNATSGO\"},\"geoparquet-items\":{\"href\":\"abfs://items/gnatsgo-tables.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},\"temporal\":{\"interval\":[[\"2020-07-01T00:00:00Z\",\"2020-07-01T00:00:00Z\"]]}},\"license\":\"CC0-1.0\",\"keywords\":[\"Soils\",\"NATSGO\",\"SSURGO\",\"STATSGO2\",\"RSS\",\"USDA\",\"United
        States\"],\"providers\":[{\"url\":\"https://www.nrcs.usda.gov/\",\"name\":\"United
        States Department of Agriculture, Natural Resources Conservation Service\",\"roles\":[\"licensor\",\"producer\",\"processor\",\"host\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"This
        collection contains the table data for gNATSGO. This table data can be used
        to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters)
        Collection.\\n\\nThe gridded National Soil Survey Geographic Database (gNATSGO)
        is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that
        provides complete coverage of the best available soils information for all
        areas of the United States and Island Territories. It was created by combining
        data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic
        Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single
        seamless ESRI file geodatabase.\\n\\nSSURGO is the SPSD flagship soils database
        that has over 100 years of field-validated detailed soil mapping data. SSURGO
        contains soils information for more than 90 percent of the United States and
        island territories, but unmapped land remains. STATSGO2 is a general soil
        map that has soils data for all of the United States and island territories,
        but the data is not as detailed as the SSURGO data. The Raster Soil Surveys
        (RSSs) are the next generation soil survey databases developed using advanced
        digital soil mapping methods.\\n\\nThe gNATSGO database is composed primarily
        of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are
        newer product with relatively limited spatial extent.  These RSSs were merged
        into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent
        of RSS is expected to increase in the coming years.\\n\\nSee the [official
        documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"table:storage_options\":{\"account_name\":\"soils\"}}},\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"chaashto\",\"description\":\"The
        Horizon AASHTO table contains the American Association of State Highway Transportation
        Officials classification(s) for the referenced horizon.  One row in this table
        is marked as the representative AASHTO classification for the horizon.\"},{\"name\":\"chconsistence\",\"description\":\"The
        Horizon Consistence table contains descriptive terms of soil consistence --
        rupture resistance, plasticity, and stickiness -- for the referenced horizon.
        \ One row in this table is marked as having the representative characteristics
        for the horizon.\"},{\"name\":\"chdesgnsuffix\",\"description\":\"The Horizon
        Designation Suffix table contains the designation suffix(es), one per row,
        \ for the referenced horizon.  For example, the \\\"h\\\" and \\\"s\\\" of
        a Bhs horizon appear as two rows in this table.\"},{\"name\":\"chfrags\",\"description\":\"The
        Horizon Fragments table lists the mineral and organic fragments that generally
        occur in the referenced horizon.  If the Volume % is greater than zero (low=5,
        RV=10, high=15) in a row, the kind and size of fragment in that row exists
        everywhere this horizon and component occur in the map unit.  If the Volume
        % includes zero (low=0, RV=5, high=10), the kind and size of fragment may
        exist in some places, but not in others.\"},{\"name\":\"chorizon\",\"description\":\"The
        Horizon table lists the horizon(s) and related data for the referenced map
        unit component.  If the horizon thickness is greater than zero (low=5, RV=8,
        high=12), the horizon exists everywhere this component occurs.  If the horizon
        thickness includes zero (low=0, RV=1, high=3), the horizon may exist in some
        places, but not in other places.\\r\\nHorizons that have two distinct parts,
        such as E/B or E&Bt horizons, are recorded twice.  Once for the characteristics
        of the first part; and again on another row, using the same depths and thicknesses,
        for the characteristics of the other part.\"},{\"name\":\"chpores\",\"description\":\"The
        Horizon Pores table lists the voids for the referenced horizon.  If the Quantity
        is greater than zero (low=2, RV=5, high=10) in a row, the voids in that row
        exist everywhere the horizon and component occur in the map unit.  If the
        Quantity includes zero (low=0, RV=2, high=5), the voids may exist in some
        places, but not in others.  More than one row can be marked as an RV row because
        a horizon may have more than one size or shape of void.\"},{\"name\":\"chstruct\",\"description\":\"The
        Horizon Structure table lists the individual soil structure size, grade, and
        shape terms for the referenced horizon.  Terms in this table are assembled
        into a structure group string which is recorded in the Horizon Structure Group
        table.\"},{\"name\":\"chstructgrp\",\"description\":\"The Horizon Structure
        Group table lists the ranges of  soil structure for the referenced horizon.
        \ The row with the typically occurring structure is marked as being representative.
        \ The entry in this table is based on grouping of entries in the Horizon Structure
        table.\"},{\"name\":\"chtext\",\"description\":\"The Horizon Text table contains
        notes and narrative descriptions related to the referenced horizon.  Some
        notes may provide additional information about the horizon for which there
        is no explicit column for such data.  In many cases, the table is empty for
        a particular horizon.\"},{\"name\":\"chtexture\",\"description\":\"The Horizon
        Texture table lists the individual texture(s), or term(s) used in lieu of
        texture, for the referenced horizon.  Only the unmodified texture terms are
        listed in the Horizon Texture table; modifiers are listed in the Horizon Texture
        Modifier table.  For example, a gravelly loamy sand is shown as \\\"GR-LS\\\"
        in the Horizon Texture Group table, \\\"ls\\\" in the Horizon Texture table,
        and \\\"gr\\\" in the Horizon Texture Modifier table.\"},{\"name\":\"chtexturegrp\",\"description\":\"The
        Horizon Texture Group table lists the range of textures for the referenced
        horizon as a concatenation of horizon texture and texture modifier(s).  For
        example, a horizon that is gravelly loamy sand in some places and gravelly
        loamy coarse sand in other places is shown as GR-LS on one row and GR-LCOS
        on another row in this table.  The row with the typically occurring texture
        is identified as the RV row.  Stratified textures are shown in one row.  For
        example, a horizon that is stratified gravelly loamy fine sand and cobbly
        coarse sand is shown as SR- GR-LFS CB-COS on one row and the Stratified? column
        for that row is marked \\\"yes\\\".  If two or more textures always occur
        together but are not stratified, all of the textures are listed on one row
        and the Stratified? column for that row is marked \\\"no\\\".\"},{\"name\":\"chtexturemod\",\"description\":\"The
        Horizon Texture Modifier table lists the texture modifier(s) for the referenced
        texture.  For example, a gravelly loamy sand is shown as \\\"GR-LS\\\" in
        the Horizon Texture Group table, \\\"ls\\\" in the Horizon Texture table,
        and \\\"gr\\\" in this table.\"},{\"name\":\"chunified\",\"description\":\"The
        Horizon Unified table contains the Unified Soil Classification(s) for the
        referenced horizon.  One row in the Horizon Unified table is marked as the
        representative Unified classification for the horizon.\"},{\"name\":\"cocanopycover\",\"description\":\"The
        Component Canopy Cover table lists the overstory plants that typically occur
        on the referenced map unit component.\"},{\"name\":\"cocropyld\",\"description\":\"The
        Component Crop Yield table lists commonly grown crops and their expected range
        in yields when grown on the referenced map unit component.  Yields for the
        map unit as a whole are given in the Mapunit Crop Yield table.\"},{\"name\":\"codiagfeatures\",\"description\":\"The
        Component Diagnostic Features table lists the typical soil features, such
        as ochric epipedon or cambic horizon, for the referenced map unit component.\"},{\"name\":\"coecoclass\",\"description\":\"The
        Component Ecological Classification table identifies the ecological sites
        typically associated with the referenced map unit component.  These may include
        the official NRCS forestland and rangland ecological sites, as well as those
        of other classification systems, such as the USFS Habitat Types.\"},{\"name\":\"coeplants\",\"description\":\"The
        Component Existing Plants table lists the plants, either rangeland or forestland
        plants, that typically occur on the referenced map unit component.\"},{\"name\":\"coerosionacc\",\"description\":\"The
        Component Erosion Accelerated table lists the kinds of accelerated erosion
        that occur on the referenced map unit component.  One row in this table is
        marked as the representative kind of accelerated erosion for that component.\"},{\"name\":\"coforprod\",\"description\":\"The
        Component Forest Productivity table lists the site index and the annual productivity
        in cubic feet per acre per year (CAMI) of forest overstory tree species that
        typically occur on the referenced map unit component.\"},{\"name\":\"coforprodo\",\"description\":\"The
        Component Forest Productivity - Other table lists the site index and annual
        productivity of forest overstory tree species in units other than cubic feet
        per acre per year for trees that typically occur on the referenced map unit
        component.\"},{\"name\":\"cogeomordesc\",\"description\":\"The Component Geomorphic
        Description table lists the geomorphic features on which the referenced map
        unit component typically occurs.\"},{\"name\":\"cohydriccriteria\",\"description\":\"The
        Component Hydric Criteria table lists the hydric soil criteria met for those
        referenced map unit components that are classified as a \\\"hydric soil.\\\"\"},{\"name\":\"cointerp\",\"description\":\"The
        Component Interpretation table lists the predictions of behavior and limiting
        features for specified uses made for the referenced map unit component.\"},{\"name\":\"comonth\",\"description\":\"The
        Component Month table lists the monthly flooding and ponding characteristics
        for the referenced map unit component.  This table has one row for each month
        of the year.\"},{\"name\":\"component\",\"description\":\"The Component table
        lists the map unit components identified in the referenced map unit, and selected
        properties of each component.  If the Component % is greater than zero (low=65,
        RV=75, high=90) for a component, that component exists in every delineation
        of that mapunit.  If the Component % includes zero (low=0, RV=50, high=90),
        the component may exist in some delineations, but not in others.\"},{\"name\":\"copm\",\"description\":\"The
        Component Parent Material table lists the individual parent material(s) for
        the referenced map unit component.    In some cases where soils developed
        in multiple materials in a vertical sequence, that sequence will be noted.
        \ In other cases multiple entries with no vertical sequence noted indicates
        the soil may have formed in one of the materials listed.\"},{\"name\":\"copmgrp\",\"description\":\"The
        Component Parent Material Group table lists the concatenated string of parent
        material(s) in which the referenced map unit component formed based on entries
        in the Component Parent Material table.  For example, a component formed in
        one parent material, such as loess, or one vertical sequence of parent materials,
        such as loamy glacial drift over silty residuum weathered from shale, has
        one row in this table.  A component formed in one parent material in some
        locations, but another parent material (or sequence of parent materials) in
        other locations has two rows in this table, one for each parent material (or
        sequence of parent materials).  One row is identified as the representative
        parent material.\"},{\"name\":\"copwindbreak\",\"description\":\"The Component
        Potential Windbreak table lists the windbreak plant species commonly recommended
        for the referenced map unit component.  A windbreak plant listed in this table
        may be used alone or in combination with other plants.\"},{\"name\":\"corestrictions\",\"description\":\"The
        Component Restrictions table lists the root restrictive feature(s) or layer(s)
        for the referenced map unit component.  If the thickness of the restrictive
        layer is greater than zero (low=5, RV=8, high=10), the restrictive layer exists
        in all delineations of the map unit where the component occurs.  If the thickness
        of the restrictive layer includes zero (low=0, RV=2, high=5), the restrictive
        layer may exist in some delineations, but not in others.  This table will
        be empty if the component does not have restrictive features, but could have
        several rows if several restrictive features occur in the soil.\"},{\"name\":\"cosoilmoist\",\"description\":\"The
        Component Soil Moisture table describes the typical soil moisture profile
        for the referenced map unit component during the month referenced in the Component
        Month table.  The soil moisture profiles for each month, taken as a group
        of twelve months, describe the representative situation for the component
        throughout the year.\"},{\"name\":\"cosoiltemp\",\"description\":\"The Component
        Soil Temperature table describes the typical soil temperature profile for
        the referenced map unit component during the month referenced in the Component
        Month table.  The soil temperature profiles for each month, taken as a group
        of twelve months, describe the representative situation for the component
        throughout the year.\"},{\"name\":\"cosurffrags\",\"description\":\"The Component
        Surface Fragments table lists the organic or mineral fragments that generally
        occur on the surface of the referenced map unit component.  If the cover percent
        is greater than zero (low=0.1, RV=1, high=3) for a row in this table, the
        fragment is in every delineation of the map unit where the referenced component
        occurs.  If the Cover % includes zero (low=0, RV=0.01, high=1) for a row in
        this table, the fragment may exist in some delineations and not in others.\"},{\"name\":\"cosurfmorphgc\",\"description\":\"The
        Component Three Dimensional Surface Morphometry table lists the typical geomorphic
        position (s) of the referenced map unit component, in three dimension terms.
        \ The geomorphic position(s) listed in this table apply to the geomorphic
        feature referenced in the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphhpp\",\"description\":\"The
        Component Two Dimensional Surface Morphometry table lists the geomorphic position(s)
        of the referenced map unit component, in two dimensional hillslope profile
        terms.  The geomorphic position(s) listed in this table apply to the geomorphic
        feature referenced  in the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphmr\",\"description\":\"The
        Component Microrelief Surface Morphometry table lists microrelief features
        associated with the referenced geomorphic (microfeature) feature shown in
        the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphss\",\"description\":\"The
        Component Slope Shape Surface Morphometry table lists the geomorphic shape(s)
        of the referenced map unit component, in slope shape terms.  The slope shape
        terms listed in this table apply to the referenced geomorphic feature shown
        in the Component Geomorphic Description table.\"},{\"name\":\"cotaxfmmin\",\"description\":\"The
        Component Taxonomic Family Mineralogy table lists the mineralogy characteristics,
        as defined in Soil Taxonomy, that apply to the referenced map unit component.\"},{\"name\":\"cotaxmoistcl\",\"description\":\"The
        Component Taxonomic Moisture Class table provides clear identification of
        the intended taxonomic moisture class, as defined in Soil Taxonomy, that apply
        to the referenced map unit component, even though moisture class is implied
        at a higher taxonomic level.  The class or classes listed in this table describe
        the representative situation for the component.\"},{\"name\":\"cotext\",\"description\":\"The
        Component Text table contains notes and narrative descriptions for the referenced
        map unit component.  In many cases, the table will be empty for a particular
        component.\"},{\"name\":\"cotreestomng\",\"description\":\"The Component Trees
        To Manage table lists the trees commonly recommended for managing on the referenced
        map unit component.\"},{\"name\":\"cotxfmother\",\"description\":\"The Component
        Taxonomic Family Other Criteria table lists the other taxonomic characteristics,
        such as classes of coatings or permanent cracks, as defined in Soil Taxonomy,
        that apply to the referenced map unit component.  The characteristics listed
        in this table describe the representative situation for the component.\"},{\"name\":\"distinterpmd\",\"description\":\"The
        Distribution Interp Metadata table records the set of NASIS fuzzy logic interpretations
        which were generated for the map unit components included in a set of distribution
        data.\"},{\"name\":\"distlegendmd\",\"description\":\"The Distribution Legend
        Metadata table records information about the legends or soil survey areas
        selected for inclusion in a set of distributed data.  The presence of a legend
        in this table does not imply that all of the available data for that legend
        was included in the set of data that was distributed.  Only certain map units
        and components for that legend may have been selected.  The record of the
        criteria used for selecting map units and components may be found in the Distribution
        Metadata table.\"},{\"name\":\"distmd\",\"description\":\"The Distribution
        Metadata table records information associated with the selection of a set
        of data for distribution to some entity or information system external to
        NASIS.  A set of distribution data may include only selected map units from
        a legend or legends, and only selected components of those map units.  This
        table records the criteria used for selecting map units and components for
        inclusion in the set of distributed data.  Other recorded information includes
        the name of the NASIS user who initiated a distribution request, and the times
        when that request was made, and when that request was ultimately processed.\"},{\"name\":\"laoverlap\",\"description\":\"The
        Legend Area Overlap table lists the geographic areas that are coincident with
        the soil survey area identified in the Legend table.  For example, a survey
        area that covers two counties would have two rows in this table, one for each
        county.  Other types of geographic areas listed might include state, MLRA,
        rainfall (R) factor area, climate (C) factor area, etc.\"},{\"name\":\"legend\",\"description\":\"The
        Legend table identifies the soil survey area that the legend is related to,
        and related information about that legend.\"},{\"name\":\"legendtext\",\"description\":\"The
        Legend Text table contains notes and narrative descriptions related to the
        referenced legend.  Legend text is optional.  In many cases, this table is
        empty.\"},{\"name\":\"mapunit\",\"description\":\"The Mapunit table identifies
        the map units included in the referenced legend.  Data related the map unit
        as a whole are also given.\"},{\"name\":\"muaggatt\",\"description\":\"The
        Mapunit Aggregated Attribute table records a variety of soil attributes and
        interpretations that have been aggregated from the component level to a single
        value at the map unit level. They have been aggregated by one or more appropriate
        means in order to express a consolidated value or interpretation for the map
        unit as a whole.\"},{\"name\":\"muaoverlap\",\"description\":\"The Mapunit
        Area Overlap table lists the map units that exist in the overlap between the
        entire soil survey area and the referenced geographic area in the Legend Area
        Overlap table.\"},{\"name\":\"mucropyld\",\"description\":\"The Mapunit Crop
        Yield table lists commonly grown crops and their expected yields for the referenced
        map unit as a whole.  Yields for individual map unit components are given
        in the Component Crop Yield table.\"},{\"name\":\"mutext\",\"description\":\"The
        Mapunit Text table contains notes and narrative descriptions related to the
        referenced map unit.\"},{\"name\":\"sacatalog\",\"description\":\"This table
        records the primary dynamic metadata associated with a soil survey area.  This
        includes such things as survey area version, tabular data version, etc. The
        remaining dynamic metadata, which soil interpretations were generated for
        the corresponding soil survey area, is recorded in the Survey Area Interpretation
        table.\"},{\"name\":\"sainterp\",\"description\":\"This table records information
        about the soil interpretations that were generated for a soil survey area.\"},{\"name\":\"valu1\",\"description\":\"Included
        with the gSSURGO database, but not a part of the standard SSURGO dataset is
        a table called Valu1. This table contains 57 pre-summarized or 'ready to map'
        attributes derived from the official SSURGO database. These attribute data
        are pre-summarized to the map unit level using best-practice generalization
        methods intended to meet the needs of most users. The generalization methods
        include map unit component weighted averages and percent of the map unit meeting
        a given criteria. These themes were prepared to better meet the mapping needs
        of users of soil survey information and can be used with both SSURGO and gridded
        SSURGO (gSSURGO) datasets.\"}],\"msft:group_id\":\"gnatsgo\",\"msft:container\":\"gnatsgo-stac\",\"stac_extensions\":[\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"soils\",\"msft:short_description\":\"The
        gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS
        Soil & Plant Science Division (SPSD) composite database that provides complete
        coverage of the best available soils information for all areas of the United
        States and Island Territories.\",\"msft:region\":\"westeurope\"},{\"id\":\"hgb\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb\"},{\"rel\":\"license\",\"href\":\"https://earthdata.nasa.gov/earth-observation-data/data-use-policy\",\"title\":\"EOSDIS
        Data Use Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/hgb\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"HGB:
        Harmonized Global Biomass for 2010\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hgb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Harmonized
        Global Biomass\"},\"geoparquet-items\":{\"href\":\"abfs://items/hgb.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-61.002778,180.0,84.0]]},\"temporal\":{\"interval\":[[\"2010-12-31T00:00:00Z\",\"2010-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"Biomass\",\"Carbon\",\"ORNL\"],\"providers\":[{\"url\":\"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763\",\"name\":\"Oak
        Ridge National Laboratory Distributed Active Archive Center\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://carbonplan.org\",\"name\":\"CarbonPlan\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[300]},\"description\":\"This
        dataset provides temporally consistent and harmonized global maps of aboveground
        and belowground biomass carbon density for the year 2010 at 300m resolution.
        The aboveground biomass map integrates land-cover-specific, remotely sensed
        maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed
        from the published literature and, where necessary, updated to cover the focal
        extent or time period. The belowground biomass map similarly integrates matching
        maps derived from each aboveground biomass map and land-cover-specific empirical
        models. Aboveground and belowground maps were then integrated separately using
        ancillary maps of percent tree/land cover and a rule-based decision tree.
        Maps reporting the accumulated uncertainty of pixel-level estimates are also
        provided.\\n\",\"item_assets\":{\"aboveground\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global above-ground
        biomass\"},\"belowground\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global
        below-ground biomass\"},\"aboveground_uncertainty\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Uncertainty
        associated with global above-ground biomass\"},\"belowground_uncertainty\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Uncertainty
        associated with global below-ground biomass\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"cpdata\",\"stac_extensions\":[\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"cpdataeuwest\",\"msft:short_description\":\"Global
        maps of aboveground and belowground biomass carbon density for the year 2010
        at 300m resolution\",\"msft:region\":\"westeurope\"},{\"id\":\"cop-dem-glo-30\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30\"},{\"rel\":\"license\",\"href\":\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\",\"title\":\"Copernicus
        DEM License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-30\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Copernicus
        DEM GLO-30\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"COP
        DEM GLO-30\"},\"geoparquet-items\":{\"href\":\"abfs://items/cop-dem-glo-30.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2021-04-22T00:00:00Z\",\"2021-04-22T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"Copernicus\",\"DEM\",\"DSM\",\"Elevation\"],\"providers\":[{\"url\":\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\",\"name\":\"European
        Space Agency\",\"roles\":[\"licensor\"]},{\"url\":\"https://registry.opendata.aws/copernicus-dem/\",\"name\":\"Sinergise\",\"roles\":[\"producer\",\"processor\"]},{\"url\":\"https://doi.org/10.5069/G9028PQB\",\"name\":\"OpenTopography\",\"roles\":[\"host\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[30],\"platform\":[\"tandem-x\"]},\"description\":\"The
        Copernicus DEM is a digital surface model (DSM), which represents the surface
        of the Earth including buildings, infrastructure, and vegetation. This DSM
        is based on radar satellite data acquired during the TanDEM-X Mission, which
        was funded by a public-private partnership between the German Aerospace Centre
        (DLR) and Airbus Defence and Space.\\n\\nCopernicus DEM is available at both
        30-meter and 90-meter resolution; this dataset has a horizontal resolution
        of approximately 30 meters.\\n\\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf)
        for more information.\\n\\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"copernicus-dem\",\"msft:container\":\"copernicus-dem-stac\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"elevationeuwest\",\"msft:short_description\":\"Copernicus
        DEM 30 is a near-global digital surface model (DSM) with a horizontal resolution
        of approximately 30 meters, derived from radar satellite data acquired during
        the TanDEM-X mission.\",\"msft:region\":\"westeurope\"},{\"id\":\"cop-dem-glo-90\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90\"},{\"rel\":\"license\",\"href\":\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\",\"title\":\"Copernicus
        DEM License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Copernicus
        DEM GLO-90\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem-glo-90.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"COP
        DEM GLO-90\"},\"geoparquet-items\":{\"href\":\"abfs://items/cop-dem-glo-90.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2021-04-22T00:00:00Z\",\"2021-04-22T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"Copernicus\",\"DEM\",\"Elevation\"],\"providers\":[{\"url\":\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\",\"name\":\"European
        Space Agency\",\"roles\":[\"licensor\"]},{\"url\":\"https://registry.opendata.aws/copernicus-dem/\",\"name\":\"Sinergise\",\"roles\":[\"producer\",\"processor\"]},{\"url\":\"https://doi.org/10.5069/G9028PQB\",\"name\":\"OpenTopography\",\"roles\":[\"host\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[90],\"platform\":[\"tandem-x\"]},\"description\":\"The
        Copernicus DEM is a digital surface model (DSM), which represents the surface
        of the Earth including buildings, infrastructure, and vegetation. This DSM
        is based on radar satellite data acquired during the TanDEM-X Mission, which
        was funded by a public-private partnership between the German Aerospace Centre
        (DLR) and Airbus Defence and Space.\\n\\nCopernicus DEM is available at both
        30-meter and 90-meter resolution; this dataset has a horizontal resolution
        of approximately 90 meters.\\n\\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf)
        for more information.\\n\\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"copernicus-dem\",\"msft:container\":\"copernicus-dem-stac\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"elevationeuwest\",\"msft:short_description\":\"Copernicus
        DEM 90 is a near-global digital surface model (DSM) with a horizontal resolution
        of approximately 90 meters, derived from radar satellite data acquired during
        the TanDEM-X mission.\",\"msft:region\":\"westeurope\"},{\"id\":\"terraclimate\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/legalcode\",\"type\":\"text/html\",\"title\":\"CC0-1.0
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        derived from the General Circulation Model (GCM) runs conducted under the
        Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the
        four \u201CTier 1\u201D greenhouse gas emissions scenarios known as Shared
        Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support
        of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
        (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP
        model runs for which daily scenarios were produced and distributed through
        the Earth System Grid Federation. The purpose of this dataset is to provide
        a set of global, high resolution, bias-corrected climate change projections
        that can be used to evaluate climate change impacts on processes that are
        sensitive to finer-scale climate gradients and the effects of local topography
        on climate conditions.\\n\\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/)
        maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6)
        where you can find more information about these datasets. Users are encouraged
        to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf),
        provided alongside the data set, where more detailed information, references
        and acknowledgements can be found.\\n\\nThis collection contains many NetCDF
        files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\\n\\n-
        **model** is the name of a modeling group (e.g. \\\"ACCESS-CM-2\\\"). See
        the `cmip6:model` summary in the STAC collection for a full list of models.\\n-
        **scenario** is one of \\\"historical\\\", \\\"ssp245\\\" or \\\"ssp585\\\".\\n-
        **variable** is one of \\\"hurs\\\", \\\"huss\\\", \\\"pr\\\", \\\"rlds\\\",
        \\\"rsds\\\", \\\"sfcWind\\\", \\\"tas\\\", \\\"tasmax\\\", \\\"tasmin\\\".\\n-
        **year** depends on the value of *scenario*. For \\\"historical\\\", the values
        range from 1950 to 2014 (inclusive). For \\\"ssp245\\\" and \\\"ssp585\\\",
        the years range from 2015 to 2100 (inclusive).\\n\\nIn addition to the NetCDF
        files, we provide some *experimental* **reference files** as collection-level
        dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\\nThese
        files include the positions of data variables within the binary NetCDF files,
        which can speed up reading the metadata. See the example notebook for more.\",\"item_assets\":{\"pr\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Precipitation\",\"description\":\"Precipitation\"},\"tas\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily
        Near-Surface Air Temperature\",\"description\":\"Daily Near-Surface Air Temperature\"},\"hurs\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Near-Surface
        Relative Humidity\",\"description\":\"Near-Surface Relative Humidity\"},\"huss\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Near-Surface
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        Radiation\"},\"rsds\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Surface
        Downwelling Shortwave Radiation\",\"description\":\"Surface Downwelling Shortwave
        Radiation\"},\"tasmax\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily
        Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface
        Air Temperature\"},\"tasmin\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily
        Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface
        Air Temperature\"},\"sfcWind\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily-Mean
        Near-Surface Wind Speed\",\"description\":\"Daily-Mean Near-Surface Wind Speed\"}},\"sci:citation\":\"Climate
        scenarios used were from the NEX-GDDP-CMIP6 dataset, prepared by the Climate
        Analytics Group and NASA Ames Research Center using the NASA Earth Exchange,
        and distributed by the NASA Center for Climate Simulation (NCCS).\",\"stac_version\":\"1.0.0\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"kg
        m-2 s-1\",\"attrs\":{\"units\":\"kg m-2 s-1\",\"comment\":\"includes both
        liquid and solid phases\",\"long_name\":\"Precipitation\",\"cell_methods\":\"area:
        time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"precipitation_flux\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Precipitation\"},\"tas\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"near-surface
        (usually, 2 meter) air temperature; derived from downscaled tasmax & tasmin\",\"long_name\":\"Daily
        Near-Surface Air Temperature\",\"cell_methods\":\"area: mean time: maximum\",\"cell_measures\":\"area:
        areacella\",\"standard_name\":\"air_temperature\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Near-Surface Air Temperature\"},\"hurs\":{\"type\":\"data\",\"unit\":\"%\",\"attrs\":{\"units\":\"%\",\"comment\":\"The
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        to ice for T<0 C.\",\"long_name\":\"Near-Surface Relative Humidity\",\"cell_methods\":\"area:
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        Humidity\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area:
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        Specific Humidity\"},\"rlds\":{\"type\":\"data\",\"unit\":\"W m-2\",\"attrs\":{\"units\":\"W
        m-2\",\"comment\":\"The surface called 'surface' means the lower boundary
        of the atmosphere. 'longwave' means longwave radiation. Downwelling radiation
        is radiation from above. It does not mean 'net downward'. When thought of
        as being incident on a surface, a radiative flux is sometimes called 'irradiance'.
        In addition, it is identical with the quantity measured by a cosine-collector
        light-meter and sometimes called 'vector irradiance'. In accordance with common
        usage in geophysical disciplines, 'flux' implies per unit area, called 'flux
        density' in physics.\",\"long_name\":\"Surface Downwelling Longwave Radiation\",\"cell_methods\":\"area:
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        time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"surface_downwelling_shortwave_flux_in_air\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface
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        s-1\",\"attrs\":{\"units\":\"m s-1\",\"comment\":\"near-surface (usually,
        10 meters) wind speed.\",\"long_name\":\"Daily-Mean Near-Surface Wind Speed\",\"cell_methods\":\"area:
        time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"wind_speed\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily-Mean
        Near-Surface Wind Speed\"}},\"msft:container\":\"nex-gddp-cmip6\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[-59.875,89.875],\"description\":\"latitude\",\"reference_system\":4326},\"lon\":{\"axis\":\"x\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[0.125,359.875],\"description\":\"longitude\",\"reference_system\":4326},\"time\":{\"step\":\"P1DT0H0M0S\",\"type\":\"temporal\",\"extent\":[\"1950-01-01T12:00:00Z\",\"2100-12-31T00:00:00Z\"],\"description\":\"time\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"nasagddp\",\"msft:short_description\":[\"Global
        downscaled climate scenarios derived from the General Circulation Model conducted
        under CMIP6.\"],\"msft:region\":\"westeurope\"},{\"id\":\"gpm-imerg-hhr\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gpm-imerg-hhr/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gpm-imerg-hhr\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP
        DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gpm-imerg-hhr\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GPM
        IMERG\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gpm-imerg-hhr.png\",\"role\":[\"thumbnail\"],\"type\":\"image/png\",\"title\":\"gpm-imerg-hhr
        thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://imerg/gpm-imerg-hhr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\"],\"description\":\"Azure
        Blob File System URI of the gpm-imerg-hhr Zarr Group on Azure Blob Storage
        for use with adlfs.\",\"xarray:open_kwargs\":{\"use_cftime\":true,\"consolidated\":true},\"xarray:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-06-01T00:00:00Z\",\"2021-05-31T23:30:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.5067/GPM/IMERG/3B-HH/06\",\"keywords\":[\"IMERG\",\"GPM\",\"Precipitation\"],\"providers\":[{\"url\":\"https://developmentseed.org/\",\"name\":\"Development
        Seed\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"https://gpm.nasa.gov/data/directory\",\"name\":\"NASA\",\"roles\":[\"producer\"]}],\"description\":\"The
        Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information
        from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation)
        to estimate precipitation over the majority of the Earth's surface. This algorithm
        is particularly valuable over the majority of the Earth's surface that lacks
        precipitation-measuring instruments on the ground. Now in the latest Version
        06 release of IMERG the algorithm fuses the early precipitation estimates
        collected during the operation of the TRMM satellite (2000 - 2015) with more
        recent precipitation estimates collected during operation of the GPM satellite
        (2014 - present). The longer the record, the more valuable it is, as researchers
        and application developers will attest. By being able to compare and contrast
        past and present data, researchers are better informed to make climate and
        weather models more accurate, better understand normal and extreme rain and
        snowfall around the world, and strengthen applications for current and future
        disasters, disease, resource management, energy production and food security.\\n\\nFor
        more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG
        Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf)
        provides more information on the algorithm, input datasets, and output products.\",\"sci:citation\":\"Huffman,
        G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG
        Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt,
        MD, Goddard Earth Sciences Data and Information Services Center (GES DISC)\",\"stac_version\":\"1.0.0\",\"cube:variables\":{\"lat_bnds\":{\"type\":\"auxiliary\",\"unit\":\"degrees_north\",\"attrs\":{\"Units\":\"degrees_north\",\"units\":\"degrees_north\",\"coordinates\":\"lat
        latv\",\"DimensionNames\":\"lat,latv\"},\"shape\":[1800,2],\"chunks\":[1800,2],\"dimensions\":[\"lat\",\"latv\"]},\"lon_bnds\":{\"type\":\"auxiliary\",\"unit\":\"degrees_east\",\"attrs\":{\"Units\":\"degrees_east\",\"units\":\"degrees_east\",\"coordinates\":\"lon
        lonv\",\"DimensionNames\":\"lon,lonv\"},\"shape\":[3600,2],\"chunks\":[3600,2],\"dimensions\":[\"lon\",\"lonv\"]},\"time_bnds\":{\"type\":\"auxiliary\",\"attrs\":{\"Units\":\"seconds
        since 1970-01-01 00:00:00 UTC\",\"coordinates\":\"time nv\",\"DimensionNames\":\"time,nv\"},\"shape\":[368160,2],\"chunks\":[12,2],\"dimensions\":[\"time\",\"nv\"]},\"randomError\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQprecipSource\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQprecipitation\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"IRprecipitation\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationCal\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQobservationTime\":{\"type\":\"data\",\"attrs\":{\"Units\":\"minutes\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationUncal\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"IRkalmanFilterWeight\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationQualityIndex\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"probabilityLiquidPrecipitation\":{\"type\":\"data\",\"unit\":\"percent\",\"attrs\":{\"Units\":\"percent\",\"units\":\"percent\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]}},\"msft:container\":\"imerg\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"type\":\"spatial\",\"extent\":[-89.94999694824219,89.94999694824219],\"reference_system\":4326},\"lon\":{\"axis\":\"x\",\"type\":\"spatial\",\"extent\":[-179.9499969482422,179.9499969482422],\"reference_system\":4326},\"time\":{\"step\":\"P0DT0H30M0S\",\"type\":\"temporal\",\"extent\":[\"2000-06-01T00:00:00Z\",\"2021-05-31T23:30:00Z\"]}},\"stac_extensions\":[\"https://stac-extensions.github.io/xarray-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"The
        Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information
        from the GPM satellite constellation to estimate precipitation over the majority
        of the Earth's surface.\",\"msft:region\":\"westeurope\"},{\"id\":\"gnatsgo-rasters\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters\"},{\"rel\":\"about\",\"href\":\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf\",\"type\":\"application/pdf\",\"title\":\"gSSURGO
        User Guide\",\"description\":\"User guide for gSSURGO\"},{\"rel\":\"about\",\"href\":\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1464658&ext=pdf\",\"type\":\"application/pdf\",\"title\":\"gNATSGO
        Overview Slides\",\"description\":\"Slides giving a high level overview of
        the gNATSGO dataset\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/\",\"title\":\"CC0
        1.0 Universal Public Domain Dedication\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"gNATSGO
        Soil Database - Rasters\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-rasters.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"gNATSGO\"},\"geoparquet-items\":{\"href\":\"abfs://items/gnatsgo-rasters.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},\"temporal\":{\"interval\":[[\"2020-07-01T00:00:00Z\",\"2020-07-01T00:00:00Z\"]]}},\"license\":\"CC0-1.0\",\"keywords\":[\"Soils\",\"NATSGO\",\"SSURGO\",\"STATSGO2\",\"RSS\",\"USDA\",\"United
        States\"],\"providers\":[{\"url\":\"https://www.nrcs.usda.gov/\",\"name\":\"United
        States Department of Agriculture, Natural Resources Conservation Service\",\"roles\":[\"licensor\",\"producer\",\"processor\",\"host\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"This
        collection contains the raster data for gNATSGO. In order to use the map unit
        values contained in the `mukey` raster asset, you'll need to join to tables
        represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables)
        Collection. Many items have commonly used values encoded in additional raster
        assets.\\n\\nThe gridded National Soil Survey Geographic Database (gNATSGO)
        is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that
        provides complete coverage of the best available soils information for all
        areas of the United States and Island Territories. It was created by combining
        data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic
        Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single
        seamless ESRI file geodatabase.\\n\\nSSURGO is the SPSD flagship soils database
        that has over 100 years of field-validated detailed soil mapping data. SSURGO
        contains soils information for more than 90 percent of the United States and
        island territories, but unmapped land remains. STATSGO2 is a general soil
        map that has soils data for all of the United States and island territories,
        but the data is not as detailed as the SSURGO data. The Raster Soil Surveys
        (RSSs) are the next generation soil survey databases developed using advanced
        digital soil mapping methods.\\n\\nThe gNATSGO database is composed primarily
        of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are
        newer product with relatively limited spatial extent.  These RSSs were merged
        into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent
        of RSS is expected to increase in the coming years.\\n\\nSee the [official
        documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)\",\"item_assets\":{\"mukey\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"mukey\",\"description\":\"Map
        unit key is the unique identifier of a record in the Mapunit table. Use this
        column to join the Component table to the Map Unit table and the Valu1 table
        to the MapUnitRaster_10m raster map layer to map valu1 themes.\"},\"aws0_5\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_5\",\"description\":\"Available
        water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        layer based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"soc0_5\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_5\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 1 or standard zone 1
        (0-5 cm depth). The concentration of organic carbon present in the soil expressed
        in grams C per square meter to a depth of 5 cm. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_5a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_5a\",\"description\":\"Thickness
        of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed
        in cm (weighted average) for the available water storage calculation. NULL
        values are presented where data are incomplete or not available.\"},\"tk0_5s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_5s\",\"description\":\"Thickness
        of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed
        in cm (weighted average) for the Soil Organic Carbon calculation. NULL values
        are presented where data are incomplete or not available.\"},\"aws0_20\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_20\",\"description\":\"Available
        water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        zone based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"aws0_30\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_30\",\"description\":\"Available
        water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        zone based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"aws5_20\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws5_20\",\"description\":\"Available
        water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        layer based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"soc0_20\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_20\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The
        concentration of organic carbon present in the soil expressed in grams C per
        square meter to a depth of 20 cm. NULL values are presented where data are
        incomplete or not available.\"},\"soc0_30\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_30\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The
        concentration of organic carbon present in the soil expressed in grams C per
        square meter to a depth of 30 cm. NULL values are presented where data are
        incomplete or not available.\"},\"soc5_20\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc5_20\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 2 (5-20 cm depth). The
        concentration of organic carbon present in the soil expressed in grams C per
        square meter for the 5-20 cm layer. NULL values are presented where data are
        incomplete or not available.\"},\"tk0_20a\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_20a\",\"description\":\"Thickness
        of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_20s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_20s\",\"description\":\"Thickness
        of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_30a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_30a\",\"description\":\"Thickness
        of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_30s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_30s\",\"description\":\"Thickness
        of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk5_20a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk5_20a\",\"description\":\"Thickness
        of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk5_20s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk5_20s\",\"description\":\"Thickness
        of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"aws0_100\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_100\",\"description\":\"Available
        water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        zone based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"aws0_150\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_150\",\"description\":\"Available
        water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        zone based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"aws0_999\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_999\",\"description\":\"Available
        water storage estimate (AWS) in total soil profile (0 cm to the reported depth
        of the soil profile), expressed in mm. The volume of plant available water
        that the soil can store in this layer based on all map unit components (weighted
        average). NULL values are presented where data are incomplete or not available.\"},\"aws20_50\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws20_50\",\"description\":\"Available
        water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        layer based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"droughty\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"droughty\",\"description\":\"zone
        for commodity crops that is less than or equal to 6 inches (152 mm) expressed
        as \\\"1\\\" for a drought vulnerable soil landscape map unit or \\\"0\\\"
        for a non-droughty soil landscape map unit or NULL for miscellaneous areas
        (includes water bodies) or where data were not available. It is computed as
        a weighted average for major earthy components. Earthy components are those
        soil series or higher level taxa components that can support crop growth (Dobos
        et al., 2012). Major components are those soil components where the majorcompflag
        = 'Yes'\"},\"nccpi3sg\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3sg\",\"description\":\"National
        Commodity Crop Productivity Index for Small Grains (weighted average) for
        major earthy components. Values range from .01 (low productivity) to .99 (high
        productivity). Earthy components are those soil series or higher level taxa
        components that can support crop growth (Dobos et al., 2012). Major components
        are those soil components where the majorcompflag = 'Yes' (SSURGO component
        table). NULL values are presented where data are incomplete or not available.\"},\"soc0_100\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_100\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard zone 4 (0-100 cm depth). The
        concentration of organic carbon present in the soil expressed in grams C per
        square meter to a depth of 100 cm. NULL values are presented where data are
        incomplete or not available.\"},\"soc0_150\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_150\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard zone 5 (0-150 cm depth). The
        concentration of organic carbon present in the soil expressed in grams C per
        square meter to a depth of 150 cm. NULL values are presented where data are
        incomplete or not available.\"},\"soc0_999\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_999\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in total soil profile (0 cm to the reported
        depth of the soil profile). The concentration of organic carbon present in
        the soil expressed in grams C per square meter for the total reported soil
        profile depth. NULL values are presented where data are incomplete or not
        available.\"},\"soc20_50\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc20_50\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 3 (20-50 cm depth).
        The concentration of organic carbon present in the soil expressed in grams
        C per square meter for the 20-50 cm layer. NULL values are presented where
        data are incomplete or not available.\"},\"tk0_100a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_100a\",\"description\":\"Thickness
        of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_100s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_100s\",\"description\":\"Thickness
        of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_150a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_150a\",\"description\":\"Thickness
        of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_150s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_150s\",\"description\":\"Thickness
        of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk0_999a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_999a\",\"description\":\"Thickness
        of soil components used in total soil profile (0 cm to the reported depth
        of the soil profile) expressed in cm (weighted average) for the available
        water storage calculation. NULL values are presented where data are incomplete
        or not available.\"},\"tk0_999s\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_999s\",\"description\":\"Thickness
        of soil components used in total soil profile (0 cm to the reported depth
        of the soil profile) expressed in cm (weighted average) for the Soil Organic
        Carbon calculation. NULL values are presented where data are incomplete or
        not available.\"},\"tk20_50a\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk20_50a\",\"description\":\"Thickness
        of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk20_50s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk20_50s\",\"description\":\"Thickness
        of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"aws50_100\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws50_100\",\"description\":\"Available
        water storage estimate (AWS) in standard layer 3 (50-100 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        layer based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"musumcpct\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpct\",\"description\":\"The
        sum of the comppct_r (SSURGO component table) values for all listed components
        in the map unit. Useful metadata information. NULL values are presented where
        data are incomplete or not available.\"},\"nccpi3all\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3all\",\"description\":\"National
        Commodity Crop Productivity Index that has the highest value among Corn and
        Soybeans, Small Grains, or Cotton (weighted average) for major earthy components.
        Values range from .01 (low productivity) to .99 (high productivity). Earthy
        components are those soil series or higher level taxa components that can
        support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"nccpi3cot\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3cot\",\"description\":\"National
        Commodity Crop Productivity Index for Cotton (weighted average) for major
        earthy components. Values range from .01 (low productivity) to .99 (high productivity).
        Earthy components are those soil series or higher level taxa components that
        can support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"nccpi3soy\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3soy\",\"description\":\"National
        Commodity Crop Productivity Index for Soybeans (weighted average) for major
        earthy components. Values range from .01 (low productivity) to .99 (high productivity).
        Earthy components are those soil series or higher level taxa components that
        can support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"pwsl1pomu\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"pwsl1pomu\",\"description\":\"Potential
        Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit
        that meets the PWSL criteria. The hydric rating (soil component variable \u201Chydricrating\u201D)
        is an indicator of wet soils. For version 1 (pwsl1), those soil components
        that meet the following criteria are tagged as PWSL and their comppct_r values
        are summed for each map unit. Soil components with hydricrating = 'YES' are
        considered PWSL. Soil components with hydricrating = \u201CNO\u201D are not
        PWSL. Soil components with hydricrating = 'UNRANKED' are tested using other
        attributes, and will be considered PWSL if any of the following conditions
        are met: drainagecl = 'Poorly drained' or 'Very poorly drained' or the localphase
        or the otherph data fields contain any of the phrases \\\"drained\\\" or \\\"undrained\\\"
        or \\\"channeled\\\" or \\\"protected\\\" or \\\"ponded\\\" or \\\"flooded\\\".
        If these criteria do not determine the PWSL for a component and hydricrating
        = 'UNRANKED', then the map unit will be classified as PWSL if the map unit
        name contains any of the phrases \\\"drained\\\" or \\\"undrained\\\" or \\\"channeled\\\"
        or \\\"protected\\\" or \\\"ponded\\\" or \\\"flooded\\\". For version 1 (pwsl1),
        waterbodies are identified as \\\"999\\\" when map unit names match a list
        of terms that identify water or intermittent water or map units have a sum
        of the comppct_r for \\\"Water\\\" that is 80% or greater. NULL values are
        presented where data are incomplete or not available.\"},\"rootznaws\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"rootznaws\",\"description\":\"Root
        zone (commodity crop) available water storage estimate (RZAWS) , expressed
        in mm, is the volume of plant available water that the soil can store within
        the root zone based on all map unit earthy major components (weighted average).
        Earthy components are those soil series or higher level taxa components that
        can support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"rootznemc\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"rootznemc\",\"description\":\"Root
        zone depth is the depth within the soil profile that commodity crop (cc) roots
        can effectively extract water and nutrients for growth. Root zone depth influences
        soil productivity significantly. Soil component horizon criteria for root-limiting
        depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan,
        sulfuric material, a dense layer, a layer having a pH of less than 3.5, or
        a layer having an electrical conductivity of more than 12 within the component
        soil profile. If no root-restricting zone is identified, a depth of 150 cm
        is used to approximate the root zone depth (Dobos et al., 2012). Root zone
        depth is computed for all map unit major earthy components (weighted average).
        Earthy components are those soil series or higher level taxa components that
        can support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"soc50_100\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc50_100\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 4 (50-100 cm depth).
        The concentration of organic carbon present in the soil expressed in grams
        C per square meter for the 50-100 cm layer. NULL values are presented where
        data are incomplete or not available.\"},\"tk50_100a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk50_100a\",\"description\":\"Thickness
        of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk50_100s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk50_100s\",\"description\":\"Thickness
        of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"aws100_150\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws100_150\",\"description\":\"Available
        water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed
        in mm. The volume of plant available water that the soil can store in this
        layer based on all map unit components (weighted average). NULL values are
        presented where data are incomplete or not available.\"},\"aws150_999\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws150_999\",\"description\":\"Available
        water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth
        of the soil profile), expressed in mm. The volume of plant available water
        that the soil can store in this layer based on all map unit components (weighted
        average). NULL values are presented where data are incomplete or not available.\"},\"musumcpcta\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpcta\",\"description\":\"The
        sum of the comppct_r (SSURGO component table) values used in the available
        water storage calculation for the map unit. Useful metadata information. NULL
        values are presented where data are incomplete or not available.\"},\"musumcpcts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpcts\",\"description\":\"The
        sum of the comppct_r (SSURGO component table) values used in the soil organic
        carbon calculation for the map unit. Useful metadata information. NULL values
        are presented where data are incomplete or not available.\"},\"nccpi3corn\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3corn\",\"description\":\"National
        Commodity Crop Productivity Index for Corn (weighted average) for major earthy
        components. Values range from .01 (low productivity) to .99 (high productivity).
        Earthy components are those soil series or higher level taxa components that
        can support crop growth (Dobos et al., 2012). Major components are those soil
        components where the majorcompflag = 'Yes' (SSURGO component table). NULL
        values are presented where data are incomplete or not available.\"},\"pctearthmc\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"pctearthmc\",\"description\":\"The
        National Commodity Crop Productivity Index map unit percent earthy is the
        map unit summed comppct_r for major earthy components. Earthy components are
        those soil series or higher level taxa components that can support crop growth
        (Dobos et al., 2012). Major components are those soil components where the
        majorcompflag = 'Yes' (SSURGO component table). Useful metadata information.
        NULL values are presented where data are incomplete or not available.\"},\"soc100_150\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc100_150\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 5 (100-150 cm depth).
        The concentration of organic carbon present in the soil expressed in grams
        C per square meter for the 100-150 cm layer. NULL values are presented where
        data are incomplete or not available.\"},\"soc150_999\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc150_999\",\"description\":\"Soil
        organic carbon stock estimate (SOC) in standard layer 6 (150 cm to the reported
        depth of the soil profile). The concentration of organic carbon present in
        the soil expressed in grams C per square meter for the 150 cm and greater
        depth layer. NULL values are presented where data are incomplete or not available.\"},\"tk100_150a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk100_150a\",\"description\":\"Thickness
        of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted
        average) for the available water storage calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk100_150s\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk100_150s\",\"description\":\"Thickness
        of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted
        average) for the Soil Organic Carbon calculation. NULL values are presented
        where data are incomplete or not available.\"},\"tk150_999a\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk150_999a\",\"description\":\"Thickness
        of soil components used in standard layer 6 (150 cm to the reported depth
        of the soil profile) expressed in cm (weighted average) for the available
        water storage calculation. NULL values are presented where data are incomplete
        or not available.\"},\"tk150_999s\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk150_999s\",\"description\":\"Thickness
        of soil components used in standard layer 6 (150 cm to the reported depth
        of the soil profile) expressed in cm (weighted average) for the Soil Organic
        Carbon calculation. NULL values are presented where data are incomplete or
        not available.\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"gnatsgo\",\"msft:container\":\"gnatsgo-stac\",\"stac_extensions\":[\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"soils\",\"msft:short_description\":\"Raster
        data for the gridded National Soil Survey Geographic Database (gNATSGO), a
        USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides
        complete coverage of the best available soils information for all areas of
        the United States and Island Territories.\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-hag\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-hag\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Height above Ground\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hag-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-hag.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"Elevation\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[2.0]},\"description\":\"This
        COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc)
        data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf)
        followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\\n\\nThe
        Height Above Ground Nearest Neighbor filter takes as input a point cloud with
        Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround,
        that contains the normalized height values.\\n\\nGround points may be generated
        with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf)
        or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf),
        but you can use any method you choose, as long as the ground returns are marked.\\n\\nNormalized
        heights are a commonly used attribute of point cloud data. This can also be
        referred to as height above ground (HAG) or above ground level (AGL) heights.
        In the end, it is simply a measure of a point's relative height as opposed
        to its raw elevation value.\\n\\nThe filter finds the number of ground points
        nearest to the non-ground point under consideration. It calculates an average
        ground height weighted by the distance of each ground point from the non-ground
        point. The HeightAboveGround is the difference between the Z value of the
        non-ground point and the interpolated ground height.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"name\":\"HeightAboveGround\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Raster
        for Height Above Ground (HAG)\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
        Lidar collection for the Height Above Ground (HAG or Elevation) Cloud Optimized
        Geotiffs (COGs). All USGS HAG COG STAC items will be associated with this
        STAC collection.\",\"msft:region\":\"westeurope\"},{\"id\":\"io-lulc-annual-v02\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02\"},{\"rel\":\"related\",\"href\":\"https://livingatlas.arcgis.com/landcover/\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC
        BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"10m
        Annual Land Use Land Cover (9-class) V2\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-annual-v02.png\",\"title\":\"10m
        Annual Land Use Land Cover (9-class)\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/io-lulc-annual-v02.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2017-01-01T00:00:00Z\",\"2024-01-01T00:00:00Z\"]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"Global\",\"Land
        Cover\",\"Land Use\",\"Sentinel\"],\"providers\":[{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"licensor\"]},{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact
        Observatory\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"raster:bands\":[{\"nodata\":0,\"spatial_resolution\":10}]},\"description\":\"Time
        series of annual global maps of land use and land cover (LULC). It currently
        has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery
        at 10m resolution. Each map is a composite of LULC predictions for 9 classes
        throughout the year in order to generate a representative snapshot of each
        year.\\n\\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/),
        Microsoft, and Esri, displays a global map of land use and land cover (LULC)
        derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017
        - 2023. Each map is a composite of LULC predictions for 9 classes throughout
        the year in order to generate a representative snapshot of each year. This
        dataset was generated by Impact Observatory, which used billions of human-labeled
        pixels (curated by the National Geographic Society) to train a deep learning
        model for land classification. Each global map was produced by applying this
        model to the Sentinel-2 annual scene collections from the Mircosoft Planetary
        Computer. Each of the maps has an assessed average accuracy of over 75%.\\n\\nThese
        maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)
        and provide a relative reduction in the amount of anomalous change between
        classes, particularly between \u201CBare\u201D and any of the vegetative classes
        \u201CTrees,\u201D \u201CCrops,\u201D \u201CFlooded Vegetation,\u201D and
        \u201CRangeland\u201D. This updated time series of annual global maps is also
        re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\\n\\nAll
        years are available under a Creative Commons BY-4.0.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global
        land cover data\",\"file:values\":[{\"values\":[0],\"summary\":\"No Data\"},{\"values\":[1],\"summary\":\"Water\"},{\"values\":[2],\"summary\":\"Trees\"},{\"values\":[4],\"summary\":\"Flooded
        vegetation\"},{\"values\":[5],\"summary\":\"Crops\"},{\"values\":[7],\"summary\":\"Built
        area\"},{\"values\":[8],\"summary\":\"Bare ground\"},{\"values\":[9],\"summary\":\"Snow/ice\"},{\"values\":[10],\"summary\":\"Clouds\"},{\"values\":[11],\"summary\":\"Rangeland\"}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"io-land-cover\",\"msft:container\":\"io-lulc\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/label/v1.0.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Global
        land cover information with 9 classes for 2017-2023 at 10m resolution\"},{\"id\":\"goes-cmi\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi\"},{\"rel\":\"license\",\"href\":\"https://www.nesdisia.noaa.gov/policy.html\",\"title\":\"Public
        Domain\"},{\"rel\":\"preview\",\"href\":\"https://planetarycomputer.microsoft.com/api/data/v1/collection/map?collection=goes-cmi\",\"title\":\"Map
        of collection mosaic\",\"type\":\"text/html\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/goes-cmi\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GOES-R
        Cloud & Moisture Imagery\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-cmi-thumb.png\",\"title\":\"GOES
        CMIP\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/goes-cmi.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W-MON\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}},\"tilejson\":{\"title\":\"Mosaic
        TileJSON with default rendering\",\"href\":\"https://planetarycomputer.microsoft.com/api/data/v1/collection/tilejson.json?collection=goes-cmi&expression=C02_2km_wm%3B0.45%2AC02_2km_wm%2B0.1%2AC03_2km_wm%2B0.45%2AC01_2km_wm%3BC01_2km_wm&nodata=-1&rescale=1%2C1000&color_formula=Gamma+RGB+2.5+Saturation+1.4+Sigmoidal+RGB+2+0.7&asset_as_band=True&resampling=bilinear&format=png\",\"type\":\"application/json\",\"roles\":[\"tiles\"]}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-81.33,6.3,81.33],[141.7,-81.33,180.0,81.33]]},\"temporal\":{\"interval\":[[\"2017-02-28T00:16:52Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"GOES\",\"NOAA\",\"NASA\",\"Satellite\",\"Cloud\",\"Moisture\"],\"providers\":[{\"url\":\"https://www.nasa.gov/content/goes\",\"name\":\"NASA\",\"roles\":[\"producer\"]},{\"url\":\"https://www.goes-r.gov/\",\"name\":\"NOAA\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"GOES-16\",\"GOES-17\",\"GOES-18\",\"GOES-19\"],\"goes:mode\":[\"3\",\"4\",\"6\"],\"instruments\":[\"ABI\"],\"goes:image-type\":[\"FULL
        DISK\",\"CONUS\",\"MESOSCALE\"],\"goes:processing-level\":[\"L2\"]},\"description\":\"The
        GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product
        provides 16 reflective and emissive bands at high temporal cadence over the
        Western Hemisphere.\\n\\nThe GOES-R series is the latest in the Geostationary
        Operational Environmental Satellites (GOES) program, which has been operated
        in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R
        Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary
        orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer.
        The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions
        between 0.5km and 2km.\\n\\n### Geographic coverage\\n\\nThe ABI captures
        three levels of coverage, each at a different temporal cadence depending on
        the modes described below. The geographic coverage for each image is described
        by the `goes:image-type` STAC Item property.\\n\\n- _FULL DISK_: a circular
        image depicting nearly full coverage of the Western Hemisphere.\\n- _CONUS_:
        a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental
        U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\\n- _MESOSCALE_:
        a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between
        two different mesoscale geographic regions.\\n\\n### Modes\\n\\nThere are
        three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and
        Mode 6.\\n\\n- Mode _3_ consists of one observation of the full disk scene
        of the Earth, three observations of the continental United States (CONUS),
        and thirty observations for each of two distinct mesoscale views every fifteen
        minutes.\\n- Mode _4_ consists of the observation of the full disk scene every
        five minutes.\\n- Mode _6_ consists of one observation of the full disk scene
        of the Earth, two observations of the continental United States (CONUS), and
        twenty observations for each of two distinct mesoscale views every ten minutes.\\n\\nThe
        mode that each image was captured with is described by the `goes:mode` STAC
        Item property.\\n\\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8)
        video for an idea of how the ABI scans multiple geographic regions over time.\\n\\n###
        Cloud and Moisture Imagery\\n\\nThe Cloud and Moisture Imagery product contains
        one or more images with pixel values identifying \\\"brightness values\\\"
        that are scaled to support visual analysis.  Cloud and Moisture Imagery product
        (CMIP) files are generated for each of the sixteen ABI reflective and emissive
        bands. In addition, there is a multi-band product file that includes the imagery
        at all bands (MCMIP).\\n\\nThe Planetary Computer STAC Collection `goes-cmi`
        captures both the CMIP and MCMIP product files into individual STAC Items
        for each observation from a GOES-R satellite. It contains the original CMIP
        and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of
        the data from each MCMIP band (2km); the full-resolution CMIP band for bands
        1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful
        for rendering.\\n\\nThis product is not in a standard coordinate reference
        system (CRS), which can cause issues with some tooling that does not handle
        non-standard large geographic regions.\\n\\n### For more information\\n- [Beginner\u2019s
        Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\\n-
        [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+
        Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish
        verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\\n\\n\",\"item_assets\":{\"C01_1km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud
        and Moisture Imagery reflectance factor - Band 01 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI
        Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land,
        coastal water mapping\",\"center_wavelength\":0.47}]},\"C01_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 01\",\"eo:bands\":[{\"name\":\"ABI Band
        1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land,
        coastal water mapping\",\"center_wavelength\":0.47}]},\"C02_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 02\",\"eo:bands\":[{\"name\":\"ABI Band
        2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation,
        winds\",\"center_wavelength\":0.64}]},\"C03_1km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud
        and Moisture Imagery reflectance factor - Band 03 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI
        Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn
        scar, aerosol over water, winds\",\"center_wavelength\":0.87}]},\"C03_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 03\",\"eo:bands\":[{\"name\":\"ABI Band
        3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn scar,
        aerosol over water, winds\",\"center_wavelength\":0.87}]},\"C04_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 04\",\"eo:bands\":[{\"name\":\"ABI Band
        4\",\"common_name\":\"cirrus\",\"description\":\"Daytime cirrus cloud\",\"center_wavelength\":1.38}]},\"C05_1km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud
        and Moisture Imagery reflectance factor - Band 05 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI
        Band 5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase
        and particle size, snow\",\"center_wavelength\":1.61}]},\"C05_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 05\",\"eo:bands\":[{\"name\":\"ABI Band
        5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase and
        particle size, snow\",\"center_wavelength\":1.61}]},\"C06_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 06\",\"eo:bands\":[{\"name\":\"ABI Band
        6\",\"common_name\":\"swir22\",\"description\":\"Daytime land, cloud properties,
        particle size, vegetation, snow\",\"center_wavelength\":2.25}]},\"C07_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 07\",\"eo:bands\":[{\"name\":\"ABI
        Band 7\",\"description\":\"Surface and cloud, fog at night, fire, winds\",\"center_wavelength\":3.89}]},\"C08_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 08\",\"eo:bands\":[{\"name\":\"ABI
        Band 8\",\"description\":\"High-level atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.17}]},\"C09_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 09\",\"eo:bands\":[{\"name\":\"ABI
        Band 9\",\"description\":\"Midlevel atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.93}]},\"C10_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 10\",\"eo:bands\":[{\"name\":\"ABI
        Band 10\",\"description\":\"Lower-level water vapor, winds, and silicon dioxide\",\"center_wavelength\":7.34}]},\"C11_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 11\",\"eo:bands\":[{\"name\":\"ABI
        Band 11\",\"description\":\"Total water for stability, cloud phase, dust,
        silicon dioxide, rainfall\",\"center_wavelength\":8.44}]},\"C12_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 12\",\"eo:bands\":[{\"name\":\"ABI
        Band 12\",\"description\":\"Total ozone, turbulence, winds\",\"center_wavelength\":9.61}]},\"C13_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 13\",\"eo:bands\":[{\"name\":\"ABI
        Band 13\",\"description\":\"Surface and clouds\",\"center_wavelength\":10.33}]},\"C14_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 14\",\"eo:bands\":[{\"name\":\"ABI
        Band 14\",\"description\":\"Imagery, sea surface temperature, clouds, rainfall\",\"center_wavelength\":11.19}]},\"C15_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 15\",\"eo:bands\":[{\"name\":\"ABI
        Band 15\",\"description\":\"Total water, volcanic ash, sea surface temperature\",\"center_wavelength\":12.27}]},\"C16_2km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery brightness temperature at top of atmosphere - Band 16\",\"eo:bands\":[{\"name\":\"ABI
        Band 16\",\"description\":\"Air temperature, cloud heights\",\"center_wavelength\":13.27}]},\"MCMIP-nc\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Cloud
        and Moisture Imagery - Multiband\",\"eo:bands\":[{\"name\":\"ABI Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime
        aerosol over land, coastal water mapping\",\"center_wavelength\":0.47},{\"name\":\"ABI
        Band 2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation,
        winds\",\"center_wavelength\":0.64},{\"name\":\"ABI Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime
        vegetation, burn scar, aerosol over water, winds\",\"center_wavelength\":0.87},{\"name\":\"ABI
        Band 4\",\"common_name\":\"cirrus\",\"description\":\"Daytime cirrus cloud\",\"center_wavelength\":1.38},{\"name\":\"ABI
        Band 5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase
        and particle size, snow\",\"center_wavelength\":1.61},{\"name\":\"ABI Band
        6\",\"common_name\":\"swir22\",\"description\":\"Daytime land, cloud properties,
        particle size, vegetation, snow\",\"center_wavelength\":2.25},{\"name\":\"ABI
        Band 7\",\"description\":\"Surface and cloud, fog at night, fire, winds\",\"center_wavelength\":3.89},{\"name\":\"ABI
        Band 8\",\"description\":\"High-level atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.17},{\"name\":\"ABI
        Band 9\",\"description\":\"Midlevel atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.93},{\"name\":\"ABI
        Band 10\",\"description\":\"Lower-level water vapor, winds, and silicon dioxide\",\"center_wavelength\":7.34},{\"name\":\"ABI
        Band 11\",\"description\":\"Total water for stability, cloud phase, dust,
        silicon dioxide, rainfall\",\"center_wavelength\":8.44},{\"name\":\"ABI Band
        12\",\"description\":\"Total ozone, turbulence, winds\",\"center_wavelength\":9.61},{\"name\":\"ABI
        Band 13\",\"description\":\"Surface and clouds\",\"center_wavelength\":10.33},{\"name\":\"ABI
        Band 14\",\"description\":\"Imagery, sea surface temperature, clouds, rainfall\",\"center_wavelength\":11.19},{\"name\":\"ABI
        Band 15\",\"description\":\"Total water, volcanic ash, sea surface temperature\",\"center_wavelength\":12.27},{\"name\":\"ABI
        Band 16\",\"description\":\"Air temperature, cloud heights\",\"center_wavelength\":13.27}],\"description\":\"The
        Cloud and Moisture Imagery product contains one or more Earth-view images
        with pixel values identifying 'brightness values' that are scaled to support
        visual analysis.\"},\"C02_0.5km\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 02 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI
        Band 2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation,
        winds\",\"center_wavelength\":0.64}]},\"C01_2km_wm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 01, Web Mercator\",\"eo:bands\":[{\"name\":\"ABI
        Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land,
        coastal water mapping\",\"center_wavelength\":0.47}]},\"C02_2km_wm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 02, Web Mercator\",\"eo:bands\":[{\"name\":\"ABI
        Band 2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation,
        winds\",\"center_wavelength\":0.64}]},\"C03_2km_wm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture
        Imagery reflectance factor - Band 03, Web Mercator\",\"eo:bands\":[{\"name\":\"ABI
        Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn
        scar, aerosol over water, winds\",\"center_wavelength\":0.87}]},\"C01_DQF_1km\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"quality-mask\"],\"title\":\"Cloud
        and Moisture Imagery data quality flags - Band 01 (full resolution)\"},\"C01_DQF_2km\":{\"type\":\"image/tiff;
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        R., Lybarger, N. D., Viger, R., Lesmes, D., Skalak, K., Brakebill, J., Cline,
        D., Dunne, K., Rasmussen, K., & Miguez-Macho, G. (2023). CONUS404: The NCAR\u2013USGS
        4-km Long-Term Regional Hydroclimate Reanalysis over the CONUS. Bulletin of
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        time\",\"description\":\"BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"BUCKET
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        for upwelling clear sky longwave flux at top accumulated since model start
        time\",\"description\":\"BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"BUCKET
        FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP\"},\"I_ACSWDNBC\":{\"type\":\"data\",\"unit\":\"J
        m-2\",\"attrs\":{\"units\":\"J m-2\",\"stagger\":\"\",\"long_name\":\"Bucket
        for downwelling clear sky shortwave flux at bottom accumulated since model
        start time\",\"description\":\"BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE
        FLUX AT BOTTOM\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"BUCKET
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        m-2\",\"attrs\":{\"units\":\"J m-2\",\"stagger\":\"\",\"long_name\":\"Bucket
        for downwelling clear sky shortwave flux at top accumulated since model start
        time\",\"description\":\"BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT
        TOP\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"BUCKET
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        for upwelling clear sky shortwave flux at bottom accumulated since model start
        time\",\"description\":\"BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT
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        m-2\",\"attrs\":{\"units\":\"J m-2\",\"stagger\":\"\",\"long_name\":\"Bucket
        for upwelling clear sky shortwave flux at top accumulated since model start
        time\",\"description\":\"BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT
        TOP\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"BUCKET
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        content in soil relative to 273.16\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"energy
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        SNOW WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME\"},\"REFL_1KM_AGL\":{\"type\":\"data\",\"unit\":\"dbZ\",\"attrs\":{\"units\":\"dbZ\",\"stagger\":\"\",\"description\":\"1KM
        AGL REFLECTIVITY\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"1KM
        AGL REFLECTIVITY\"},\"GRAUPEL_ACC_NC\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"stagger\":\"\",\"long_name\":\"Accumulated
        graupel water equivalent, accumulated over prior 60 minutes\",\"description\":\"ACCUMULATED
        GRAUPEL WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME\"},\"shape\":[376944,1015,1367],\"chunks\":[1,1015,1367],\"dimensions\":[\"time\",\"south_north\",\"west_east\"],\"description\":\"ACCUMULATED
        GRAUPEL WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME\"}},\"msft:container\":\"noaa\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"type\":\"spatial\",\"extent\":[17.647308349609375,57.34341812133789]},\"lon\":{\"axis\":\"x\",\"type\":\"spatial\",\"extent\":[-138.73135375976562,-57.068634033203125]},\"time\":{\"step\":\"P0DT1H0M0S\",\"type\":\"temporal\",\"extent\":[\"1979-10-01T00:00:00Z\",\"2022-09-30T23:00:00Z\"],\"description\":\"time\"},\"west_east\":{\"type\":\"count\",\"extent\":[0,1366],\"description\":\"west_east
        index\"},\"bottom_top\":{\"type\":\"count\",\"extent\":[0,49],\"description\":\"bottom_top
        index\"},\"south_north\":{\"type\":\"count\",\"extent\":[0,1014],\"description\":\"south_north
        index\"},\"west_east_stag\":{\"type\":\"count\",\"extent\":[0,1367],\"description\":\"west_east_stag
        index.\"},\"bottom_top_stag\":{\"type\":\"count\",\"extent\":[0,50],\"description\":\"bottom_top_stag
        index\"},\"snow_layers_stag\":{\"type\":\"count\",\"extent\":[0,2],\"description\":\"snow
        layer index.\"},\"snso_layers_stag\":{\"type\":\"count\",\"extent\":[0,6],\"description\":\"snso
        layer index.\"},\"soil_layers_stag\":{\"type\":\"count\",\"extent\":[0,4],\"description\":\"soil
        layer index.\"},\"south_north_stag\":{\"type\":\"count\",\"extent\":[0,1015],\"description\":\"south_north_stag
        index.\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/xarray-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.2.0/schema.json\"],\"msft:storage_account\":\"azureopendatastorage\",\"msft:short_description\":\"High-resolution
        hydro-climate dataset for forcing hydrological models and meteorological analysis
        over the conterminous United States.\"},{\"id\":\"sentinel-1-rtc\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-rtc/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-rtc\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"CC
        BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel
        1 Radiometrically Terrain Corrected (RTC)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-1-rtc.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Sentinel
        1 RTC\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-1-rtc.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2014-10-10T00:28:21Z\",null]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"ESA\",\"Copernicus\",\"Sentinel\",\"C-Band\",\"SAR\",\"RTC\"],\"providers\":[{\"url\":\"https://catalyst.earth\",\"name\":\"Catalyst\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"licensor\"]}],\"summaries\":{\"platform\":[\"SENTINEL-1A\",\"SENTINEL-1B\",\"SENTINEL-1C\"],\"constellation\":[\"Sentinel-1\"],\"s1:resolution\":[\"high\"],\"s1:orbit_source\":[\"DOWNLINK\",\"POEORB\",\"PREORB\",\"RESORB\"],\"sar:looks_range\":[5],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"sar:product_type\":[\"GRD\"],\"sar:looks_azimuth\":[1],\"sar:polarizations\":[[\"VV\",\"VH\"],[\"HH\",\"HV\"],[\"VV\"],[\"VH\"],[\"HH\"]],\"sar:frequency_band\":[\"C\"],\"s1:processing_level\":[\"1\"],\"sar:instrument_mode\":[\"IW\"],\"sar:center_frequency\":[5.405],\"sar:resolution_range\":[20],\"s1:product_timeliness\":[\"NRT-10m\",\"NRT-1h\",\"NRT-3h\",\"Fast-24h\",\"Off-line\",\"Reprocessing\"],\"sar:resolution_azimuth\":[22],\"sar:pixel_spacing_range\":[10],\"sar:observation_direction\":[\"right\"],\"sar:pixel_spacing_azimuth\":[10],\"sar:looks_equivalent_number\":[4.4],\"sat:platform_international_designator\":[\"2014-016A\",\"2016-025A\",\"0000-000A\"]},\"description\":\"The
        [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission
        is a constellation of two polar-orbiting satellites, operating day and night
        performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically
        Terrain Corrected (RTC) data in this collection is a radiometrically terrain
        corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd)
        products produced by the European Space Agency. The RTC processing is performed
        by [Catalyst](https://catalyst.earth/).\\n\\nRadiometric Terrain Correction
        accounts for terrain variations that affect both the position of a given point
        on the Earth's surface and the brightness of the radar return, as expressed
        in radar geometry. Without treatment, the hill-slope modulations of the radiometry
        threaten to overwhelm weaker thematic land cover-induced backscatter differences.
        Additionally, comparison of backscatter from multiple satellites, modes, or
        tracks loses meaning.\\n\\nA Planetary Computer account is required to retrieve
        SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed)
        for more information.\\n\\n### Methodology\\n\\nThe Sentinel-1 GRD product
        is converted to calibrated intensity using the conversion algorithm described
        in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration
        of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf).
        The flat earth calibration values for gamma correction (i.e. perpendicular
        to the radar line of sight) are extracted from the GRD metadata. The calibration
        coefficients are applied as a two-dimensional correction in range (by sample
        number) and azimuth (by time). All available polarizations are calibrated
        and written as separate layers of a single file. The calibrated SAR output
        is reprojected to nominal map orientation with north at the top and west to
        the left.\\n\\nThe data is then radiometrically terrain corrected using PlanetDEM
        as the elevation source. The correction algorithm is nominally based upon
        D. Small, [\u201CFlattening Gamma: Radiometric Terrain Correction for SAR
        Imagery\u201D](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf),
        IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August
        2011, pp 3081-3093. For each image scan line, the digital elevation model
        is interpolated to determine the elevation corresponding to the position associated
        with the known near slant range distance and arc length for each input pixel.
        The elevations at the four corners of each pixel are estimated using bilinear
        resampling. The four elevations are divided into two triangular facets and
        reprojected onto the plane perpendicular to the radar line of sight to provide
        an estimate of the area illuminated by the radar for each earth flattened
        pixel. The uncalibrated sum at each earth flattened pixel is normalized by
        dividing by the flat earth surface area. The adjustment for gamma intensity
        is given by dividing the normalized result by the cosine of the incident angle.
        Pixels which are not illuminated by the radar due to the viewing geometry
        are flagged as shadow.\\n\\nCalibrated data is then orthorectified to the
        appropriate UTM projection. The orthorectified output maintains the original
        sample sizes (in range and azimuth) and was not shifted to any specific grid.\\n\\nRTC
        data is processed only for the Interferometric Wide Swath (IW) mode, which
        is the main acquisition mode over land and satisfies the majority of service
        requirements.\\n\",\"item_assets\":{\"hh\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HH: horizontal
        transmit, horizontal receive\",\"description\":\"Terrain-corrected gamma naught
        values of signal transmitted with horizontal polarization and received with
        horizontal polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"hv\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HV:
        horizontal transmit, vertical receive\",\"description\":\"Terrain-corrected
        gamma naught values of signal transmitted with horizontal polarization and
        received with vertical polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"vh\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VH:
        vertical transmit, horizontal receive\",\"description\":\"Terrain-corrected
        gamma naught values of signal transmitted with vertical polarization and received
        with horizontal polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"vv\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VV:
        vertical transmit, vertical receive\",\"description\":\"Terrain-corrected
        gamma naught values of signal transmitted with vertical polarization and received
        with vertical polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-1\",\"msft:container\":\"sentinel1-grd-rtc\",\"stac_extensions\":[\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\",\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"sentinel1euwestrtc\",\"msft:requires_account\":true,\"msft:short_description\":\"Radiometrically
        terrain corrected SAR imagery derived from the Sentinel 1 Level 1 GRD product.\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-intensity\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-intensity/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-intensity\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-intensity\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Intensity\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/intensity-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"Intensity\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[2.0]},\"description\":\"This
        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It is a collection of Cloud Optimized GeoTIFFs representing the pulse return
        magnitude.\\n\\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html)
        and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)
        and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"name\":\"Intensity\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"uint16\",\"description\":\"Intensity
        PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
        Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the Intensity
        dimension.\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-pointsourceid\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-pointsourceid/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-pointsourceid\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-pointsourceid\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Point Source\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/pointsourceid-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-pointsourceid.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"PointSourceId\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[5.0]},\"description\":\"This
        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It is a collection of Cloud Optimized GeoTIFFs representing the file source
        ID from which the point originated. Zero indicates that the point originated
        in the current file.\\n\\nThis values are based on the PointSourceId [PDAL
        dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)
        and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"name\":\"PointSourceId\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"int32\",\"description\":\"PointSourceId
        PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
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        reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ
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        COPC files.\\n\\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud)
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        Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io)
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        access and cloud streaming.\\n\\nLPC can be summarized for construction of
        digital terrain models (DTM), filtered for extraction of features like vegetation
        and buildings, and visualized to provide a point cloud map of the physical
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        compute and extract a variety of landscape characterization products, and
        some of them are provided by Planetary Computer, including Height Above Ground,
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        tiles represent a one-to-one mapping of original tiled content as provided
        by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with
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        UTM zones for their location without adjustment to the vertical datum. In
        some cases, vertical datum description may not match actual data values, especially
        for pre-2010 USGS 3DEP point cloud data.\\n\\nIn addition to these COPC files,
        various higher-level derived products are available as Cloud Optimized GeoTIFFs
        in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).\",\"item_assets\":{\"data\":{\"type\":\"application/vnd.laszip+copc\",\"roles\":[\"data\"],\"title\":\"COPC
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        burn occurred with values assigned to unburned land pixels and additional
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        Terms of Use of Research Data\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/alos-fnf-mosaic\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ALOS
        Forest/Non-Forest Annual Mosaic\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/alos-fnf-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"ALOS
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        global 25m resolution SAR mosaics and forest/non-forest maps are free and
        open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm)
        using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing
        Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS
        PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\\n\\nThe
        global forest/non-forest maps (FNF) were generated by a Random Forest machine
        learning-based classification method, with the re-processed global 25m resolution
        [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic)
        (Ver. 2.0.0) as input. Here, the \\\"forest\\\" is defined as the tree covered
        land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance
        with the FAO definition of forest. The classification results are presented
        in four categories, with two categories of forest areas: forests with a canopy
        cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending
        on the density of the forest area.\\n\\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf)
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        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-returns\",\"title\":\"Human
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        3DEP Lidar Returns\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/numberofreturns-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
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        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It is a collection of Cloud Optimized GeoTIFFs representing the number of
        returns for a given pulse.\\n\\nThis values are based on the PointSourceId
        [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)
        and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to remove outliers and noise.\\n\\nThe values are based on the NumberOfReturns
        [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)
        and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"name\":\"ReturnNumber\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"int16\",\"description\":\"Raster
        for ReturnNumber PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
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        Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the ReturnNumber
        dimension.\",\"msft:region\":\"westeurope\"},{\"id\":\"mobi\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi\"},{\"rel\":\"license\",\"href\":\"https://aka.ms/mobi-license\",\"title\":\"NatureServe
        MoBI license\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/mobi\",\"title\":\"Human
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        Map of Biodiversity Importance\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/mobi.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MoBI\"},\"geoparquet-items\":{\"href\":\"abfs://items/mobi.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-130.23751729209377,21.736663926419077,-63.66198266211968,49.184200486300604]]},\"temporal\":{\"interval\":[[\"2020-04-14T00:00:00Z\",\"2020-04-14T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"MoBI\",\"Natureserve\",\"United
        States\",\"Biodiversity\"],\"providers\":[{\"url\":\"http://natureserve.org/\",\"name\":\"NatureServe\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[990]},\"description\":\"The
        [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance)
        (MoBI) consists of raster maps that combine habitat information for 2,216
        imperiled species occurring in the conterminous United States, using weightings
        based on range size and degree of protection to identify areas of high importance
        for biodiversity conservation. Species included in the project are those which,
        as of September 2018, had a global conservation status of G1 (critical imperiled)
        or G2 (imperiled) or which are listed as threatened or endangered at the full
        species level under the United States Endangered Species Act. Taxonomic groups
        included in the project are vertebrates (birds, mammals, amphibians, reptiles,
        turtles, crocodilians, and freshwater and anadromous fishes), vascular plants,
        selected aquatic invertebrates (freshwater mussels and crayfish) and selected
        pollinators (bumblebees, butterflies, and skippers).\\n\\nThere are three
        types of spatial data provided, described in more detail below: species richness,
        range-size rarity, and protection-weighted range-size rarity.  For each type,
        this data set includes five different layers &ndash; one for all species combined,
        and four additional layers that break the data down by taxonomic group (vertebrates,
        plants, freshwater invertebrates, and pollinators) &ndash; for a total of
        fifteen layers.\\n\\nThese data layers are intended to identify areas of high
        potential value for on-the-ground biodiversity protection efforts. As a synthesis
        of predictive models, they cannot guarantee either the presence or absence
        of imperiled species at a given location. For site-specific decision-making,
        these data should be used in conjunction with field surveys and/or documented
        occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\\n\",\"item_assets\":{\"RSR_All\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size
        rarity for all species\"},\"RSR_Plants\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity
        for vascular plants\"},\"RSR_Vertebrates\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity
        for vertebrates\"},\"RSR_AquaticInverts\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity
        for aquatic invertebrates\"},\"PWRSR_GAP12_SUM_All\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted
        range-size rarity for all species\"},\"SpeciesRichness_All\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species
        richness for all species\"},\"RSR_PollinatorInverts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size
        rarity for pollinators\"},\"PWRSR_GAP12_SUM_Plants\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted
        range-size rarity for vascular plants\"},\"SpeciesRichness_Plants\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species
        richness for vascular plants\"},\"PWRSR_GAP12_SUM_Vertebrates\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted
        range-size rarity for vertebrates\"},\"SpeciesRichness_Vertebrates\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species
        richness for vertebrates\"},\"PWRSR_GAP12_SUM_AquaticInverts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted
        range-size rarity for aquatic invertebrates\"},\"SpeciesRichness_AquaticInverts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species
        richness for aquatic invertebrates\"},\"PWRSR_GAP12_SUM_PollinatorInverts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted
        range-size rarity for pollinators\"},\"SpeciesRichness_PollinatorInverts\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species
        richness for pollinators\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"mobi\",\"stac_extensions\":[\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Habitat
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        States\",\"msft:region\":\"westeurope\"},{\"id\":\"landsat-c2-l2\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5066/P9IAXOVV\",\"title\":\"Landsat
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        green (Thematic Mapper)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"name\":\"TM_B3\",\"common_name\":\"red\",\"description\":\"Visible
        red (Thematic Mapper)\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"name\":\"TM_B4\",\"common_name\":\"nir08\",\"description\":\"Near
        infrared (Thematic Mapper)\",\"center_wavelength\":0.83,\"full_width_half_max\":0.14},{\"name\":\"TM_B5\",\"common_name\":\"swir16\",\"description\":\"Short-wave
        infrared (Thematic Mapper)\",\"center_wavelength\":1.65,\"full_width_half_max\":0.2},{\"name\":\"TM_B6\",\"common_name\":\"lwir\",\"description\":\"Long-wave
        infrared (Thematic Mapper)\",\"center_wavelength\":11.45,\"full_width_half_max\":2.1},{\"name\":\"TM_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave
        infrared (Thematic Mapper)\",\"center_wavelength\":2.22,\"full_width_half_max\":0.27},{\"name\":\"ETM_B1\",\"common_name\":\"blue\",\"description\":\"Visible
        blue (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.48,\"full_width_half_max\":0.07},{\"name\":\"ETM_B2\",\"common_name\":\"green\",\"description\":\"Visible
        green (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"name\":\"ETM_B3\",\"common_name\":\"red\",\"description\":\"Visible
        red (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"name\":\"ETM_B4\",\"common_name\":\"nir08\",\"description\":\"Near
        infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.84,\"full_width_half_max\":0.13},{\"name\":\"ETM_B5\",\"common_name\":\"swir16\",\"description\":\"Short-wave
        infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":1.65,\"full_width_half_max\":0.2},{\"name\":\"ETM_B6\",\"common_name\":\"lwir\",\"description\":\"Long-wave
        infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":11.34,\"full_width_half_max\":2.05},{\"name\":\"ETM_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave
        infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":2.2,\"full_width_half_max\":0.28},{\"name\":\"OLI_B1\",\"common_name\":\"coastal\",\"description\":\"Coastal/Aerosol
        (Operational Land Imager)\",\"center_wavelength\":0.44,\"full_width_half_max\":0.02},{\"name\":\"OLI_B2\",\"common_name\":\"blue\",\"description\":\"Visible
        blue (Operational Land Imager)\",\"center_wavelength\":0.48,\"full_width_half_max\":0.06},{\"name\":\"OLI_B3\",\"common_name\":\"green\",\"description\":\"Visible
        green (Operational Land Imager)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.06},{\"name\":\"OLI_B4\",\"common_name\":\"red\",\"description\":\"Visible
        red (Operational Land Imager)\",\"center_wavelength\":0.65,\"full_width_half_max\":0.04},{\"name\":\"OLI_B5\",\"common_name\":\"nir08\",\"description\":\"Near
        infrared (Operational Land Imager)\",\"center_wavelength\":0.87,\"full_width_half_max\":0.03},{\"name\":\"OLI_B6\",\"common_name\":\"swir16\",\"description\":\"Short-wave
        infrared (Operational Land Imager)\",\"center_wavelength\":1.61,\"full_width_half_max\":0.09},{\"name\":\"OLI_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave
        infrared (Operational Land Imager)\",\"center_wavelength\":2.2,\"full_width_half_max\":0.19},{\"name\":\"TIRS_B10\",\"common_name\":\"lwir11\",\"description\":\"Long-wave
        infrared (Thermal Infrared Sensor)\",\"center_wavelength\":10.9,\"full_width_half_max\":0.59}],\"platform\":[\"landsat-4\",\"landsat-5\",\"landsat-7\",\"landsat-8\",\"landsat-9\"],\"instruments\":[\"tm\",\"etm+\",\"oli\",\"tirs\"],\"view:off_nadir\":{\"maximum\":15,\"minimum\":0}},\"description\":\"Landsat
        Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products),
        consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance)
        and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature)
        image data. Collection 2 Level-2 Science Products are available from August
        22, 1982 to present.\\n\\nThis dataset represents the global archive of Level-2
        data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2)
        acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/)
        onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/)
        onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/)
        and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/)
        onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/)
        format.\\n\",\"item_assets\":{\"qa\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Surface Temperature
        Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Quality Assessment
        Band (ST_QA) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"kelvin\",\"scale\":0.01,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"ang\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Angle
        Coefficients File\",\"description\":\"Collection 2 Level-1 Angle Coefficients
        File\"},\"red\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Red
        Band\",\"eo:bands\":[{\"common_name\":\"red\",\"description\":\"Visible red\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"blue\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Blue
        Band\",\"eo:bands\":[{\"common_name\":\"blue\",\"description\":\"Visible blue\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"drad\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Downwelled
        Radiance Band\",\"description\":\"Collection 2 Level-2 Downwelled Radiance
        Band (ST_DRAD) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"watt/steradian/square_meter/micrometer\",\"scale\":0.001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"emis\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Emissivity
        Band\",\"description\":\"Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface
        Temperature Product\",\"raster:bands\":[{\"unit\":\"emissivity coefficient\",\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"emsd\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Emissivity
        Standard Deviation Band\",\"description\":\"Collection 2 Level-2 Emissivity
        Standard Deviation Band (ST_EMSD) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"emissivity
        coefficient\",\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"lwir\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"temperature\"],\"title\":\"Surface
        Temperature Band\",\"eo:bands\":[{\"common_name\":\"lwir\",\"description\":\"Long-wave
        infrared\"}],\"description\":\"Collection 2 Level-2 Thermal Infrared Band
        (ST_B6) Surface Temperature\",\"raster:bands\":[{\"unit\":\"kelvin\",\"scale\":0.00341802,\"nodata\":0,\"offset\":149.0,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"trad\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Thermal
        Radiance Band\",\"description\":\"Collection 2 Level-2 Thermal Radiance Band
        (ST_TRAD) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"watt/steradian/square_meter/micrometer\",\"scale\":0.001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"urad\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Upwelled
        Radiance Band\",\"description\":\"Collection 2 Level-2 Upwelled Radiance Band
        (ST_URAD) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"watt/steradian/square_meter/micrometer\",\"scale\":0.001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"atran\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Atmospheric
        Transmittance Band\",\"description\":\"Collection 2 Level-2 Atmospheric Transmittance
        Band (ST_ATRAN) Surface Temperature Product\",\"raster:bands\":[{\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"cdist\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud
        Distance Band\",\"description\":\"Collection 2 Level-2 Cloud Distance Band
        (ST_CDIST) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"kilometer\",\"scale\":0.01,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"green\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Green
        Band\",\"eo:bands\":[{\"common_name\":\"green\",\"description\":\"Visible
        green\",\"center_wavelength\":0.56}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"nir08\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Near
        Infrared Band 0.8\",\"eo:bands\":[{\"common_name\":\"nir08\",\"description\":\"Near
        infrared\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"lwir11\":{\"gsd\":100,\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"temperature\"],\"title\":\"Surface
        Temperature Band\",\"eo:bands\":[{\"name\":\"TIRS_B10\",\"common_name\":\"lwir11\",\"description\":\"Long-wave
        infrared\",\"center_wavelength\":10.9,\"full_width_half_max\":0.59}],\"description\":\"Collection
        2 Level-2 Thermal Infrared Band (ST_B10) Surface Temperature\",\"raster:bands\":[{\"unit\":\"kelvin\",\"scale\":0.00341802,\"nodata\":0,\"offset\":149.0,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"swir16\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Short-wave
        Infrared Band 1.6\",\"eo:bands\":[{\"common_name\":\"swir16\",\"description\":\"Short-wave
        infrared\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"swir22\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Short-wave
        Infrared Band 2.2\",\"eo:bands\":[{\"common_name\":\"swir22\",\"description\":\"Short-wave
        infrared\"}],\"description\":\"Collection 2 Level-2 Short-wave Infrared Band
        2.2 (SR_B7) Surface Reflectance\",\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"coastal\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Coastal/Aerosol
        Band\",\"eo:bands\":[{\"name\":\"OLI_B1\",\"common_name\":\"coastal\",\"description\":\"Coastal/Aerosol\",\"center_wavelength\":0.44,\"full_width_half_max\":0.02}],\"description\":\"Collection
        2 Level-2 Coastal/Aerosol Band (SR_B1) Surface Reflectance\",\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"mtl.txt\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Product
        Metadata File (txt)\",\"description\":\"Collection 2 Level-2 Product Metadata
        File (txt)\"},\"mtl.xml\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product
        Metadata File (xml)\",\"description\":\"Collection 2 Level-2 Product Metadata
        File (xml)\"},\"cloud_qa\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"cloud\",\"cloud-shadow\",\"snow-ice\",\"water-mask\"],\"title\":\"Cloud
        Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Cloud Quality
        Assessment Band (SR_CLOUD_QA) Surface Reflectance Product\",\"raster:bands\":[{\"unit\":\"bit
        index\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:bitfields\":[{\"name\":\"ddv\",\"length\":1,\"offset\":0,\"classes\":[{\"name\":\"not_ddv\",\"value\":0,\"description\":\"Pixel
        has no DDV\"},{\"name\":\"ddv\",\"value\":1,\"description\":\"Pixel has DDV\"}],\"description\":\"Dense
        Dark Vegetation (DDV)\"},{\"name\":\"cloud\",\"length\":1,\"offset\":1,\"classes\":[{\"name\":\"not_cloud\",\"value\":0,\"description\":\"Pixel
        has no cloud\"},{\"name\":\"cloud\",\"value\":1,\"description\":\"Pixel has
        cloud\"}],\"description\":\"Cloud mask\"},{\"name\":\"cloud_shadow\",\"length\":1,\"offset\":2,\"classes\":[{\"name\":\"not_shadow\",\"value\":0,\"description\":\"Pixel
        has no cloud shadow\"},{\"name\":\"shadow\",\"value\":1,\"description\":\"Pixel
        has cloud shadow\"}],\"description\":\"Cloud shadow mask\"},{\"name\":\"cloud_adjacent\",\"length\":1,\"offset\":3,\"classes\":[{\"name\":\"not_adjacent\",\"value\":0,\"description\":\"Pixel
        is not adjacent to cloud\"},{\"name\":\"adjacent\",\"value\":1,\"description\":\"Pixel
        is adjacent to cloud\"}],\"description\":\"Cloud adjacency\"},{\"name\":\"snow\",\"length\":1,\"offset\":4,\"classes\":[{\"name\":\"not_snow\",\"value\":0,\"description\":\"Pixel
        is not snow\"},{\"name\":\"shadow\",\"value\":1,\"description\":\"Pixel is
        snow\"}],\"description\":\"Snow mask\"},{\"name\":\"water\",\"length\":1,\"offset\":5,\"classes\":[{\"name\":\"not_water\",\"value\":0,\"description\":\"Pixel
        is not water\"},{\"name\":\"water\",\"value\":1,\"description\":\"Pixel is
        water\"}],\"description\":\"Water mask\"}]},\"mtl.json\":{\"type\":\"application/json\",\"roles\":[\"metadata\"],\"title\":\"Product
        Metadata File (json)\",\"description\":\"Collection 2 Level-2 Product Metadata
        File (json)\"},\"qa_pixel\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"cloud\",\"cloud-shadow\",\"snow-ice\",\"water-mask\"],\"title\":\"Pixel
        Quality Assessment Band\",\"description\":\"Collection 2 Level-1 Pixel Quality
        Assessment Band (QA_PIXEL)\",\"raster:bands\":[{\"unit\":\"bit index\",\"nodata\":1,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"qa_radsat\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"saturation\"],\"raster:bands\":[{\"unit\":\"bit
        index\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"qa_aerosol\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data-mask\",\"water-mask\"],\"title\":\"Aerosol
        Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Aerosol Quality
        Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product\",\"raster:bands\":[{\"unit\":\"bit
        index\",\"nodata\":1,\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:bitfields\":[{\"name\":\"fill\",\"length\":1,\"offset\":0,\"classes\":[{\"name\":\"not_fill\",\"value\":0,\"description\":\"Pixel
        is not fill\"},{\"name\":\"fill\",\"value\":1,\"description\":\"Pixel is fill\"}],\"description\":\"Image
        or fill data\"},{\"name\":\"retrieval\",\"length\":1,\"offset\":1,\"classes\":[{\"name\":\"not_valid\",\"value\":0,\"description\":\"Pixel
        retrieval is not valid\"},{\"name\":\"valid\",\"value\":1,\"description\":\"Pixel
        retrieval is valid\"}],\"description\":\"Valid aerosol retrieval\"},{\"name\":\"water\",\"length\":1,\"offset\":2,\"classes\":[{\"name\":\"not_water\",\"value\":0,\"description\":\"Pixel
        is not water\"},{\"name\":\"water\",\"value\":1,\"description\":\"Pixel is
        water\"}],\"description\":\"Water mask\"},{\"name\":\"interpolated\",\"length\":1,\"offset\":5,\"classes\":[{\"name\":\"not_interpolated\",\"value\":0,\"description\":\"Pixel
        is not interpolated aerosol\"},{\"name\":\"interpolated\",\"value\":1,\"description\":\"Pixel
        is interpolated aerosol\"}],\"description\":\"Aerosol interpolation\"},{\"name\":\"level\",\"length\":2,\"offset\":6,\"classes\":[{\"name\":\"climatology\",\"value\":0,\"description\":\"No
        aerosol correction applied\"},{\"name\":\"low\",\"value\":1,\"description\":\"Low
        aerosol level\"},{\"name\":\"medium\",\"value\":2,\"description\":\"Medium
        aerosol level\"},{\"name\":\"high\",\"value\":3,\"description\":\"High aerosol
        level\"}],\"description\":\"Aerosol level\"}]},\"atmos_opacity\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Atmospheric
        Opacity Band\",\"description\":\"Collection 2 Level-2 Atmospheric Opacity
        Band (SR_ATMOS_OPACITY) Surface Reflectance Product\",\"raster:bands\":[{\"scale\":0.001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"landsat\",\"msft:container\":\"landsat-c2\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/view/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"landsateuwest\",\"msft:short_description\":\"Landsat
        Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4
        and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the
        Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat
        8 and 9.\",\"msft:region\":\"westeurope\"},{\"id\":\"era5-pds\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds\"},{\"rel\":\"license\",\"href\":\"https://apps.ecmwf.int/datasets/licences/copernicus/\",\"type\":\"application/pdf\",\"title\":\"License
        to Use Copernicus Products\"},{\"rel\":\"describedby\",\"href\":\"https://confluence.ecmwf.int/display/CKB/ERA5\",\"type\":\"text/html\",\"title\":\"Project
        homepage\"},{\"rel\":\"describedby\",\"href\":\"https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it\",\"type\":\"text/html\",\"title\":\"How
        to cite\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/era5-pds\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ERA5
        - PDS\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/era5-thumbnail.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/era5-pds.parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"media_type\":\"application/x-parquet\",\"description\":\"Snapshot
        of the collection's STAC items exported to GeoParquet format.\",\"extra_fields\":{\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"1979-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"ERA5\",\"ECMWF\",\"Precipitation\",\"Temperature\",\"Reanalysis\",\"Weather\"],\"providers\":[{\"url\":\"https://www.ecmwf.int/\",\"name\":\"ECMWF\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetos.com/\",\"name\":\"Planet
        OS\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"era5:kind\":[\"fc\",\"an\"]},\"description\":\"ERA5
        is the fifth generation ECMWF atmospheric reanalysis of the global climate\\ncovering
        the period from January 1950 to present. ERA5 is produced by the\\nCopernicus
        Climate Change Service (C3S) at ECMWF.\\n\\nReanalysis combines model data
        with observations from across the world into a\\nglobally complete and consistent
        dataset using the laws of physics. This\\nprinciple, called data assimilation,
        is based on the method used by numerical\\nweather prediction centres, where
        every so many hours (12 hours at ECMWF) a\\nprevious forecast is combined
        with newly available observations in an optimal\\nway to produce a new best
        estimate of the state of the atmosphere, called\\nanalysis, from which an
        updated, improved forecast is issued. Reanalysis works\\nin the same way,
        but at reduced resolution to allow for the provision of a\\ndataset spanning
        back several decades. Reanalysis does not have the constraint\\nof issuing
        timely forecasts, so there is more time to collect observations, and\\nwhen
        going further back in time, to allow for the ingestion of improved versions\\nof
        the original observations, which all benefit the quality of the reanalysis\\nproduct.\\n\\nThis
        dataset was converted to Zarr by [Planet OS](https://planetos.com/).\\nSee
        [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\\nfor
        more.\\n\\n## STAC Metadata\\n\\nTwo types of data variables are provided:
        \\\"forecast\\\" (`fc`) and \\\"analysis\\\" (`an`).\\n\\n* An **analysis**,
        of the atmospheric conditions, is a blend of observations\\n  with a previous
        forecast. An analysis can only provide\\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\n
        \ parameters (parameters valid at a specific time, e.g temperature at 12:00),\\n
        \ but not accumulated parameters, mean rates or min/max parameters.\\n* A
        **forecast** starts with an analysis at a specific time (the 'initialization\\n
        \ time'), and a model computes the atmospheric conditions for a number of\\n
        \ 'forecast steps', at increasing 'validity times', into the future. A forecast\\n
        \ can provide\\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\n
        \ parameters, accumulated parameters, mean rates, and min/max parameters.\\n\\nEach
        [STAC](https://stacspec.org/) item in this collection covers a single month\\nand
        the entire globe. There are two STAC items per month, one for each type of
        data\\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties
        to\\nindicate which kind of variables that STAC item catalogs.\\n\\n## How
        to acknowledge, cite and refer to ERA5\\n\\nAll users of data on the Climate
        Data Store (CDS) disks (using either the web interface or the CDS API) must
        provide clear and visible attribution to the Copernicus programme and are
        asked to cite and reference the dataset provider:\\n\\nAcknowledge according
        to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\\n\\nCite
        each dataset used as indicated on the relevant CDS entries (see link to \\\"Citation\\\"
        under References on the Overview page of the dataset entry).\\n\\nThroughout
        the content of your publication, the dataset used is referred to as Author
        (YYYY).\\n\\nThe 3-steps procedure above is illustrated with this example:
        [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\\n\\nFor
        complete details, please refer to [How to acknowledge and cite a Climate Data
        Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).\",\"item_assets\":{\"surface_air_pressure\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Surface
        pressure\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"sea_surface_temperature\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Sea
        surface temperature\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"eastward_wind_at_10_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"10
        metre U wind component\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"air_temperature_at_2_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"2
        metre temperature\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"eastward_wind_at_100_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"100
        metre U wind component\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"northward_wind_at_10_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"10
        metre V wind component\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"northward_wind_at_100_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"100
        metre V wind component\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"air_pressure_at_mean_sea_level\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Mean
        sea level pressure\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"dew_point_temperature_at_2_metres\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"2
        metre dewpoint temperature\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"precipitation_amount_1hour_Accumulation\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Total
        precipitation\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"air_temperature_at_2_metres_1hour_Maximum\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Maximum
        temperature at 2 metres since previous post-processing\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"air_temperature_at_2_metres_1hour_Minimum\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Minimum
        temperature at 2 metres since previous post-processing\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Surface
        solar radiation downwards\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}}},\"stac_version\":\"1.0.0\",\"cube:variables\":{\"time1_bounds\":{\"type\":\"data\",\"attrs\":{},\"shape\":[null,2],\"dimensions\":[\"time\",\"nv\"]},\"surface_air_pressure\":{\"type\":\"data\",\"unit\":\"Pa\",\"attrs\":{\"units\":\"Pa\",\"nameCDM\":\"Surface_pressure_surface\",\"long_name\":\"Surface
        pressure\",\"nameECMWF\":\"Surface pressure\",\"product_type\":\"analysis\",\"standard_name\":\"surface_air_pressure\",\"shortNameECMWF\":\"sp\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface
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        in the upper air and near surface.\",\"msft:region\":\"westeurope\"},{\"id\":\"chloris-biomass\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chloris-biomass/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chloris-biomass\"},{\"rel\":\"license\",\"href\":\"https://spdx.org/licenses/CC-BY-NC-SA-4.0.html\",\"title\":\"Creative
        Commons Attribution Non Commercial Share Alike 4.0 International\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/chloris-biomass\",\"title\":\"Human
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        Biomass\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chloris-biomass.jpg\",\"type\":\"image/jpg\",\"roles\":[\"thumbnail\"],\"title\":\"Chloris
        Biomass\"},\"geoparquet-items\":{\"href\":\"abfs://items/chloris-biomass.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
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        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-179.95,-60,179.95,90]]},\"temporal\":{\"interval\":[[\"2003-07-31T00:00:00Z\",\"2019-07-31T00:00:00Z\"]]}},\"license\":\"CC-BY-NC-SA-4.0\",\"keywords\":[\"Chloris\",\"Biomass\",\"MODIS\",\"Carbon\"],\"providers\":[{\"url\":\"http://chloris.earth/\",\"name\":\"Chloris\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[4633]},\"description\":\"The
        Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and
        change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems.
        It covers the period 2003 - 2019, at annual time steps. The global dataset
        has a circa 4.6 km spatial resolution.\\n\\nThe maps and data sets were generated
        by combining multiple remote sensing measurements from space borne satellites,
        processed using state-of-the-art machine learning and statistical methods,
        validated with field data from multiple countries. The dataset provides direct
        estimates of aboveground stock and change, and are not based on land use or
        land cover area change, and as such they include gains and losses of carbon
        stock in all types of woody vegetation - whether natural or plantations.\\n\\nAnnual
        stocks are expressed in units of tons of biomass. Annual changes in stocks
        are expressed in units of CO2 equivalent, i.e., the amount of CO2 released
        from or taken up by terrestrial ecosystems for that specific pixel.\\n\\nThe
        spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass)
        under a Creative Common license of the type Attribution-Non Commercial-Share
        Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\\n\\n[Chloris
        Geospatial](https://chloris.earth/) is a mission-driven technology company
        that develops software and data products on the state of natural capital for
        use by business, governments, and the social sector.\\n\",\"item_assets\":{\"biomass\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        estimates of aboveground woody biomass.\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":2147483647,\"data_type\":\"uint32\"}]},\"biomass_wm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        estimates of aboveground woody biomass (Web Mercator).\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":2147483647,\"data_type\":\"uint32\"}]},\"biomass_change\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        estimates of changes (gains and losses) in aboveground woody biomass from
        the previous year.\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":-32768,\"data_type\":\"int16\"}]},\"biomass_change_wm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        estimates of changes (gains and losses) in aboveground woody biomass from
        the previous year (Web Mercator).\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":-32768,\"data_type\":\"int16\"}]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"chloris-biomass\",\"stac_extensions\":[\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"The
        Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and
        change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems
        during the period 2003 - 2019, at annual time steps. The global dataset has
        a circa 4.6 km spatial resolution.\",\"msft:region\":\"westeurope\"},{\"id\":\"kaza-hydroforecast\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast\"},{\"rel\":\"license\",\"href\":\"https://cdla.dev/sharing-1-0/\",\"type\":\"text/html\",\"title\":\"Community
        Data License Agreement \u2013 Sharing, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/kaza-hydroforecast\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"HydroForecast
        - Kwando & Upper Zambezi Rivers\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/kaza-hydroforecast-thumbnail.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[21.04494,-17.792517,23.343421,-13.08062],[23.343421,-17.792517,23.343421,-17.792517],[23.343421,-17.792517,23.343421,-17.792517],[21.04494,-15.13158,21.04494,-15.13158],[21.80157,-16.01209,21.80157,-16.01209],[22.91715,-17.38856,22.91715,-17.38856],[23.00610530305009,-17.347398048598034,23.00610530305009,-17.347398048598034],[22.669092,-14.971843,22.669092,-14.971843],[22.675487,-13.08062,22.675487,-13.08062]]},\"temporal\":{\"interval\":[[\"2022-01-01T00:00:00Z\",null]]}},\"license\":\"CDLA-Sharing-1.0\",\"version\":\"1.0.0\",\"keywords\":[\"Water\",\"HydroForecast\",\"Streamflow\",\"Hydrology\",\"Upstream
        Tech\"],\"providers\":[{\"url\":\"https://www.upstream.tech\",\"name\":\"Upstream
        Tech\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"This
        dataset is a daily updated set of HydroForecast seasonal river flow forecasts
        at six locations in the Kwando and Upper Zambezi river basins. More details
        about the locations, project context, and to interactively view current and
        previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\\n\\n##
        Flow forecast dataset and model description\\n\\n[HydroForecast](https://www.upstream.tech/hydroforecast)
        is a theory-guided machine learning hydrologic model that predicts streamflow
        in basins across the world. For the Kwando and Upper Zambezi, HydroForecast
        makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview).
        The model's output is probabilistic and the mean, median and a range of quantiles
        are available at each forecast step.\\n\\nThe underlying model has the following
        attributes: \\n\\n* Timestep: 10 days\\n* Horizon: 10 to 180 days \\n* Update
        frequency: daily\\n* Units: cubic meters per second (m\xB3/s)\\n    \\n##
        Site details\\n\\nThe model produces output for six locations in the Kwando
        and Upper Zambezi river basins.\\n\\n* Upper Zambezi sites\\n    * Zambezi
        at Chavuma\\n    * Luanginga at Kalabo\\n* Kwando basin sites\\n    * Kwando
        at Kongola -- total basin flows\\n    * Kwando Sub-basin 1\\n    * Kwando
        Sub-basin 2 \\n    * Kwando Sub-basin 3\\n    * Kwando Sub-basin 4\\n    *
        Kwando Kongola Sub-basin\\n\\n## STAC metadata\\n\\nThere is one STAC item
        per location. Each STAC item has a single asset linking to a Parquet file
        in Azure Blob Storage.\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Forecast
        Data\",\"description\":\"All seasonal river flow forecasts for a specific
        forecast site, produced by Upstream Tech's HydroForecast model\"}},\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"initialization_time\",\"type\":\"timestamp\",\"description\":\"The
        initial timestamp in the forecast model which created this forecast step.
        All rows with the same initialization time are part of the same \\\"forecast\\\"
        and are produced at the same time.\"},{\"name\":\"valid_time\",\"type\":\"timestamp\",\"description\":\"The
        point in time this forecast step is predicting. valid_time = initialization_time
        + lead_time_hours. The discharge values in this row are the average rate from
        this time (inclusive) until the next valid time (exclusive).\"},{\"name\":\"lead_time_hours\",\"type\":\"int64\",\"description\":\"The
        number of hours ahead of the initialization time this forecast step is predicting.\"},{\"name\":\"discharge_mean\",\"type\":\"double\",\"description\":\"The
        mean of the probabilistic streamflow prediction, as an average rate across
        the forecast step in m\xB3/s.\"},{\"name\":\"discharge_median\",\"type\":\"double\",\"description\":\"The
        median of the probabilistic streamflow prediction, as an average rate across
        the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.01\",\"type\":\"double\",\"description\":\"The
        quantile 0.01 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.025\",\"type\":\"double\",\"description\":\"The
        quantile 0.025 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.05\",\"type\":\"double\",\"description\":\"The
        quantile 0.05 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.1\",\"type\":\"double\",\"description\":\"The
        quantile 0.1 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.25\",\"type\":\"double\",\"description\":\"The
        quantile 0.25 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.5\",\"type\":\"double\",\"description\":\"The
        quantile 0.5 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.75\",\"type\":\"double\",\"description\":\"The
        quantile 0.75 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.9\",\"type\":\"double\",\"description\":\"The
        quantile 0.9 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.95\",\"type\":\"double\",\"description\":\"The
        quantile 0.95 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.975\",\"type\":\"double\",\"description\":\"The
        quantile 0.975 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.99\",\"type\":\"double\",\"description\":\"The
        quantile 0.99 of the probabilistic streamflow prediction, as an average rate
        across the forecast step in m\xB3/s.\"}],\"msft:container\":\"hydroforecast\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Seasonal
        river flow forecasts for the Kwando and Upper Zambezi rivers in the KAZA region
        of Africa, produced by Upstream Tech's HydroForecast model.\",\"msft:region\":\"westeurope\"},{\"id\":\"planet-nicfi-analytic\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic\"},{\"rel\":\"license\",\"href\":\"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf\",\"type\":\"application/pdf\",\"title\":\"Participant
        License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf\",\"type\":\"application/pdf\",\"title\":\"Participant
        License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://www.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI
        Program - Satellite Imagery and Monitoring | Planet\"},{\"rel\":\"documentation\",\"href\":\"https://developers.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI
        Program Resource Center\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-analytic\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Planet-NICFI
        Basemaps (Analytic)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-analytic-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/planet-nicfi-analytic.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-34.161818157002,180.0,30.145127179625]]},\"temporal\":{\"interval\":[[\"2015-12-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Planet\",\"NICFI\",\"Satellite\",\"Tropics\",\"Imagery\"],\"providers\":[{\"url\":\"http://planet.com\",\"name\":\"Planet\",\"roles\":[\"processor\",\"producer\",\"licensor\"],\"description\":\"Contact
        Planet at [planet.com/contact-sales](https://www.planet.com/contact-sales/)\"},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[4.77],\"eo:bands\":[{\"name\":\"Blue\",\"common_name\":\"blue\",\"description\":\"visible
        blue\"},{\"name\":\"Green\",\"common_name\":\"green\",\"description\":\"visible
        green\"},{\"name\":\"Red\",\"common_name\":\"red\",\"description\":\"visible
        red\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}],\"planet-nicfi:cadence\":[\"biannual\",\"monthly\"],\"planet-nicfi:percent_covered\":{\"maximum\":100,\"minimum\":0}},\"description\":\"*Note:
        Assets in this collection are only available to winners of the [GEO-Microsoft
        Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528).
        Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/)
        and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\n\\nThrough
        Norway\u2019s International Climate & Forests Initiative (NICFI), users can
        access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s
        tropics in order to help reduce and reverse the loss of tropical forests,
        combat climate change, conserve biodiversity, and facilitate sustainable development.\\n\\nIn
        support of NICFI\u2019s mission, you can use this data for a number of projects
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        world\u2019s tropical forests and the critical services they provide.\\n*
        Implement and improve policies for sustainable forest management and land
        use in developing tropical forest countries and jurisdictions.\\n* Increase
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        regrowth, restoration, and enhancement, and facilitating sustainable development,
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        in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average
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        The 8-day compositing period was chosen because twice that period is the exact
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        DAAC\",\"roles\":[\"producer\"]}],\"description\":\"Gridded estimates of daily
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        the close of a calendar year. Data are in a Lambert conformal conic projection
        for North America and are distributed in Zarr and NetCDF formats, compliant
        with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\\n\\nUse
        the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840)
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        coordinate of projection\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"ProjectedCRS\",\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"base_crs\":{\"name\":\"undefined\",\"datum\":{\"name\":\"undefined\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"id\":{\"code\":9802,\"authority\":\"EPSG\"},\"name\":\"Lambert
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        of 2nd standard parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude
        of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude
        of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting
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        day based on local time\"}},\"msft:group_keys\":[\"daily\",\"puerto rico\"],\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\"],\"msft:storage_account\":\"daymeteuwest\",\"msft:short_description\":\"Daily
        surface weather data on a 1-km grid for Puerto Rico\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-dtm-native\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm-native\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Digital Terrain Model (Native)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-dtm-native-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"DTM\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[2.0]},\"description\":\"This
        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It creates a Digital Terrain Model (DTM) using the vendor provided (native)
        ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)
        to output a collection of Cloud Optimized GeoTIFFs, removing all points that
        have been classified as noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"name\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Raster
        for Z PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
        Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs
        (COGs) using vendor provided ground classification.\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-classification\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-classification\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Classification\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-classification-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"Classification\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[5.0]},\"description\":\"This
        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry
        and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm).
        See [LAS specification](https://www.ogc.org/standards/LAS) for details.\\n\\nThis
        COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html)
        and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html)
        to select a subset of interesting classifications. Do note that not all LiDAR
        collections contain a full compliment of classification labels.\\nTo remove
        outliers, the PDAL pipeline uses a noise filter and then outputs the Classification
        dimension.\\n\\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets)
        and [`classification`](https://github.com/stac-extensions/classification)
        extensions. These classes are displayed in the \\\"Item assets\\\" below.
        You can programmatically access the full list of class values and descriptions
        using the `classification:classes` field form the `data` asset on the STAC
        collection.\\n\\nClassification rasters were produced as a subset of LiDAR
        classification categories:\\n\\n```\\n0, Never Classified\\n1, Unclassified\\n2,
        Ground\\n3, Low Vegetation\\n4, Medium Vegetation\\n5, High Vegetation\\n6,
        Building\\n9, Water\\n10, Rail\\n11, Road\\n17, Bridge Deck\\n```\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
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        Vegetation\"},{\"value\":6,\"description\":\"Building\"},{\"value\":9,\"description\":\"Water\"},{\"value\":10,\"description\":\"Rail\"},{\"value\":11,\"description\":\"Road\"},{\"value\":17,\"description\":\"Bridge
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        Lidar collection for Cloud Optimized Geotiffs (COGs) created according to
        PDAL's Intensity dimension\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-dtm\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About
        3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        3DEP Lidar Digital Terrain Model\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dtm-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-dtm.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\"temporal\":{\"interval\":[[\"2012-01-01T00:00:00Z\",\"2022-01-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"3DEP\",\"COG\",\"DTM\"],\"providers\":[{\"name\":\"Landrush\",\"roles\":[\"processor\",\"producer\"]},{\"url\":\"https://www.usgs.gov/core-science-systems/ngp/3dep/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[2.0]},\"description\":\"This
        collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).
        It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf)
        to output a collection of Cloud Optimized GeoTIFFs.\\n\\nThe Simple Morphological
        Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG
        data\",\"raster:bands\":[{\"type\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Z
        PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP
        Lidar COG\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"3dep-lidar\",\"msft:container\":\"usgs-3dep-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"usgslidareuwest\",\"msft:short_description\":\"3DEP
        Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs
        (COGs) using PDAL's filters.smrf.\",\"msft:region\":\"westeurope\"},{\"id\":\"gap\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits\",\"title\":\"Public
        Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gap\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        Gap Land Cover\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png\",\"type\":\"image/png\",\"roles\":[\"data\"],\"title\":\"USGS
        GAP\"},\"geoparquet-items\":{\"href\":\"abfs://items/gap.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}},\"original-data-ak\":{\"href\":\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_ak.zip\",\"type\":\"application/zip\",\"roles\":[\"data\"],\"title\":\"Alaska
        source data\",\"description\":\"Original source data for Alaska\"},\"original-data-hi\":{\"href\":\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_hi.zip\",\"type\":\"application/zip\",\"roles\":[\"data\"],\"title\":\"Hawaii
        source data\",\"description\":\"Original source data for Hawaii\"},\"original-data-pr\":{\"href\":\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/pr_landcover.zip\",\"type\":\"application/zip\",\"roles\":[\"data\"],\"title\":\"Puerto
        Rico source data\",\"description\":\"Original source data for Puerto Rico\"},\"original-data-conus\":{\"href\":\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gap_landfire_nationalterrestrialecosystems2011.zip\",\"type\":\"application/zip\",\"roles\":[\"data\"],\"title\":\"CONUS
        source data\",\"description\":\"Original source data for the continental United
        States (CONUS)\"}},\"extent\":{\"spatial\":{\"bbox\":[[-127.9710481801793,22.797789263564383,-65.26634281147894,51.64692620669362],[-178.13166387448902,49.09079265233118,179.87849702345594,71.43382483774205],[-160.26640694607218,18.851824447510786,-154.66974350173518,22.295114188194738],[-67.9573345827195,17.874066536543,-65.21836408976736,18.5296513469496]]},\"temporal\":{\"interval\":[[\"1999-01-01T00:00:00Z\",\"2011-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USGS\",\"GAP\",\"LANDFIRE\",\"Land
        Cover\",\"United States\"],\"providers\":[{\"url\":\"https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[30],\"label:classes\":[{\"classes\":[\"0\",\"South
        Florida Bayhead Swamp\",\"South Florida Cypress Dome\",\"South Florida Dwarf
        Cypress Savanna\",\"South Florida Mangrove Swamp\",\"South Florida Hardwood
        Hammock\",\"Southeast Florida Coastal Strand and Maritime Hammock\",\"Southwest
        Florida Coastal Strand and Maritime Hammock\",\"South Florida Pine Rockland\",\"Atlantic
        Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory\",\"Atlantic
        Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\",\"Atlantic
        Coastal Plain Upland Longleaf Pine Woodland\",\"Atlantic Coastal Plain Xeric
        River Dune\",\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland
        - Open Understory Modifier\",\"East Gulf Coastal Plain Interior Upland Longleaf
        Pine Woodland - Scrub/Shrub Modifier\",\"Florida Longleaf Pine Sandhill -
        Scrub/Shrub Understory Modifier\",\"Florida Longleaf Pine Sandhill- Open Understory
        Modifier\",\"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\",\"Atlantic
        Coastal Plain Central Maritime Forest\",\"Atlantic Coastal Plain Southern
        Maritime Forest\",\"Central and South Texas Coastal Fringe Forest and Woodland\",\"East
        Gulf Coastal Plain Limestone Forest\",\"East Gulf Coastal Plain Maritime Forest\",\"East
        Gulf Coastal Plain Southern Loess Bluff Forest\",\"East Gulf Coastal Plain
        Southern Mesic Slope Forest\",\"Mississippi Delta Maritime Forest\",\"Southern
        Coastal Plain Dry Upland Hardwood Forest\",\"Southern Coastal Plain Oak Dome
        and Hammock\",\"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe
        Forest and Woodland\",\"West Gulf Coastal Plain Mesic Hardwood Forest\",\"East-Central
        Texas Plains Pine Forest and Woodland\",\"West Gulf Coastal Plain Pine-Hardwood
        Forest\",\"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest
        and Woodland\",\"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine
        Woodland - Loblolly Modifier\",\"Deciduous Plantations\",\"East Gulf Coastal
        Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier\",\"East
        Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood
        Modifier\",\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood
        Modifier\",\"Evergreen Plantation or Managed Pine\",\"California Central Valley
        Mixed Oak Savanna\",\"California Coastal Closed-Cone Conifer Forest and Woodland\",\"California
        Coastal Live Oak Woodland and Savanna\",\"California Lower Montane Blue Oak-Foothill
        Pine Woodland and Savanna\",\"Central and Southern California Mixed Evergreen
        Woodland\",\"Mediterranean California Lower Montane Black Oak-Conifer Forest
        and Woodland\",\"Southern California Oak Woodland and Savanna\",\"Madrean
        Encinal\",\"Madrean Pinyon-Juniper Woodland\",\"Madrean Pine-Oak Forest and
        Woodland\",\"Madrean Upper Montane Conifer-Oak Forest and Woodland\",\"Edwards
        Plateau Dry-Mesic Slope Forest and Woodland\",\"Edwards Plateau Limestone
        Savanna and Woodland\",\"Edwards Plateau Mesic Canyon\",\"Llano Uplift Acidic
        Forest, Woodland and Glade\",\"East Cascades Oak-Ponderosa Pine Forest and
        Woodland\",\"Mediterranean California Mixed Evergreen Forest\",\"Mediterranean
        California Mixed Oak Woodland\",\"North Pacific Dry Douglas-fir-(Madrone)
        Forest and Woodland\",\"North Pacific Oak Woodland\",\"Edwards Plateau Limestone
        Shrubland\",\"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood\",\"Allegheny-Cumberland
        Dry Oak Forest and Woodland - Pine Modifier\",\"Central and Southern Appalachian
        Montane Oak Forest\",\"Central and Southern Appalachian Northern Hardwood
        Forest\",\"Central Appalachian Oak and Pine Forest\",\"Crosstimbers Oak Forest
        and Woodland\",\"East Gulf Coastal Plain Black Belt Calcareous Prairie and
        Woodland - Woodland Modifier\",\"East Gulf Coastal Plain Northern Dry Upland
        Hardwood Forest\",\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory
        Upland - Hardwood Modifier\",\"East Gulf Coastal Plain Northern Loess Plain
        Oak-Hickory Upland - Juniper Modifier\",\"East-Central Texas Plains Post Oak
        Savanna and Woodland\",\"Lower Mississippi River Dune Woodland and Forest\",\"Mississippi
        River Alluvial Plain Dry-Mesic Loess Slope Forest\",\"North-Central Interior
        Dry Oak Forest and Woodland\",\"North-Central Interior Dry-Mesic Oak Forest
        and Woodland\",\"Northeastern Interior Dry Oak Forest - Mixed Modifier\",\"Northeastern
        Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\",\"Northeastern Interior
        Dry Oak Forest-Hardwood Modifier\",\"Northeastern Interior Dry-Mesic Oak Forest\",\"Northern
        Atlantic Coastal Plain Dry Hardwood Forest\",\"Crowley's Ridge Sand Forest\",\"Ouachita
        Montane Oak Forest\",\"Ozark-Ouachita Dry Oak Woodland\",\"Ozark-Ouachita
        Dry-Mesic Oak Forest\",\"Southern and Central Appalachian Oak Forest\",\"Southern
        and Central Appalachian Oak Forest - Xeric\",\"Southern Interior Low Plateau
        Dry-Mesic Oak Forest\",\"Southern Ridge and Valley Dry Calcareous Forest\",\"Southern
        Ridge and Valley Dry Calcareous Forest - Pine modifier\",\"East Gulf Coastal
        Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\",\"Managed
        Tree Plantation\",\"Ruderal forest\",\"Southern Piedmont Dry Oak-(Pine) Forest
        - Loblolly Pine Modifier\",\"Acadian Low-Elevation Spruce-Fir-Hardwood Forest\",\"Acadian-Appalachian
        Montane Spruce-Fir Forest\",\"Appalachian Hemlock-Hardwood Forest\",\"Central
        and Southern Appalachian Spruce-Fir Forest\",\"0\",\"Laurentian-Acadian Northern
        Hardwoods Forest\",\"Laurentian-Acadian Northern Pine-(Oak) Forest\",\"Laurentian-Acadian
        Pine-Hemlock-Hardwood Forest\",\"Paleozoic Plateau Bluff and Talus\",\"Southern
        Appalachian Northern Hardwood Forest\",\"Atlantic Coastal Plain Dry and Dry-Mesic
        Oak Forest\",\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland
        - Offsite Hardwood\",\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak
        Forest - Hardwood Modifier\",\"East Gulf Coastal Plain Interior Shortleaf
        Pine-Oak Forest - Mixed Modifier\",\"Ozark-Ouachita Shortleaf Pine-Bluestem
        Woodland\",\"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland\",\"Southeastern
        Interior Longleaf Pine Woodland\",\"Southern Appalachian Low Mountain Pine
        Forest\",\"Southern Piedmont Dry Oak-(Pine) Forest\",\"Southern Piedmont Dry
        Oak-(Pine) Forest - Hardwood Modifier\",\"Southern Piedmont Dry Oak-(Pine)
        Forest - Mixed Modifier\",\"Southern Piedmont Dry Oak-Heath Forest - Mixed
        Modifier\",\"Eastern Great Plains Tallgrass Aspen Parkland\",\"Northwestern
        Great Plains Aspen Forest and Parkland\",\"Northwestern Great Plains Shrubland\",\"Western
        Great Plains Dry Bur Oak Forest and Woodland\",\"Western Great Plains Wooded
        Draw and Ravine\",\"Southern Atlantic Coastal Plain Mesic Hardwood Forest\",\"East
        Gulf Coastal Plain Northern Loess Bluff Forest\",\"East Gulf Coastal Plain
        Northern Mesic Hardwood Forest\",\"North-Central Interior Beech-Maple Forest\",\"North-Central
        Interior Maple-Basswood Forest\",\"Ozark-Ouachita Mesic Hardwood Forest\",\"South-Central
        Interior Mesophytic Forest\",\"Southern and Central Appalachian Cove Forest\",\"Crowley's
        Ridge Mesic Loess Slope Forest\",\"Southern Piedmont Mesic Forest\",\"Appalachian
        Shale Barrens\",\"Atlantic Coastal Plain Northern Maritime Forest\",\"Laurentian
        Pine-Oak Barrens\",\"Northeastern Interior Pine Barrens\",\"Northern Atlantic
        Coastal Plain Pitch Pine Barrens\",\"Southern Appalachian Montane Pine Forest
        and Woodland\",\"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland\",\"Middle
        Rocky Mountain Montane Douglas-fir Forest and Woodland\",\"Northern Rocky
        Mountain Dry-Mesic Montane Mixed Conifer Forest\",\"Northern Rocky Mountain
        Foothill Conifer Wooded Steppe\",\"Northern Rocky Mountain Mesic Montane Mixed
        Conifer Forest\",\"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna\",\"Northern
        Rocky Mountain Western Larch Savanna\",\"Northwestern Great Plains - Black
        Hills Ponderosa Pine Woodland and Savanna\",\"Rocky Mountain Foothill Limber
        Pine-Juniper Woodland\",\"Inter-Mountain Basins Aspen-Mixed Conifer Forest
        and Woodland\",\"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland\",\"Northern
        Rocky Mountain Subalpine Woodland and Parkland\",\"Rocky Mountain Aspen Forest
        and Woodland\",\"Rocky Mountain Lodgepole Pine Forest\",\"Rocky Mountain Poor-Site
        Lodgepole Pine Forest\",\"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest
        and Woodland\",\"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\",\"Rocky
        Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\",\"Rocky Mountain
        Bigtooth Maple Ravine Woodland\",\"Southern Rocky Mountain Dry-Mesic Montane
        Mixed Conifer Forest and Woodland\",\"Southern Rocky Mountain Mesic Montane
        Mixed Conifer Forest and Woodland\",\"Southern Rocky Mountain Ponderosa Pine
        Savanna\",\"Southern Rocky Mountain Ponderosa Pine Woodland\",\"California
        Montane Jeffrey Pine-(Ponderosa Pine) Woodland\",\"Klamath-Siskiyou Lower
        Montane Serpentine Mixed Conifer Woodland\",\"Klamath-Siskiyou Upper Montane
        Serpentine Mixed Conifer Woodland\",\"Mediterranean California Dry-Mesic Mixed
        Conifer Forest and Woodland\",\"Mediterranean California Mesic Mixed Conifer
        Forest and Woodland\",\"Sierran-Intermontane Desert Western White Pine-White
        Fir Woodland\",\"California Coastal Redwood Forest\",\"North Pacific Broadleaf
        Landslide Forest and Shrubland\",\"North Pacific Dry-Mesic Silver Fir-Western
        Hemlock-Douglas-fir Forest\",\"North Pacific Hypermaritime Sitka Spruce Forest\",\"North
        Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\",\"North Pacific
        Lowland Mixed Hardwood-Conifer Forest and Woodland\",\"North Pacific Maritime
        Dry-Mesic Douglas-fir-Western Hemlock Forest\",\"North Pacific Maritime Mesic-Wet
        Douglas-fir-Western Hemlock Forest\",\"North Pacific Mesic Western Hemlock-Silver
        Fir Forest\",\"North Pacific Wooded Volcanic Flowage\",\"Mediterranean California
        Red Fir Forest\",\"Mediterranean California Subalpine Woodland\",\"North Pacific
        Maritime Mesic Subalpine Parkland\",\"North Pacific Mountain Hemlock Forest\",\"Northern
        California Mesic Subalpine Woodland\",\"Northern Pacific Mesic Subalpine Woodland\",\"Sierra
        Nevada Subalpine Lodgepole Pine Forest and Woodland\",\"Columbia Plateau Western
        Juniper Woodland and Savanna\",\"Great Basin Pinyon-Juniper Woodland\",\"Inter-Mountain
        Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\",\"Inter-Mountain
        Basins Juniper Savanna\",\"Colorado Plateau Pinyon-Juniper Shrubland\",\"Colorado
        Plateau Pinyon-Juniper Woodland\",\"Southern Rocky Mountain Juniper Woodland
        and Savanna\",\"Southern Rocky Mountain Pinyon-Juniper Woodland\",\"Northwestern
        Great Plains Floodplain\",\"Northwestern Great Plains Riparian\",\"Western
        Great Plains Floodplain\",\"Western Great Plains Floodplain Systems\",\"Western
        Great Plains Riparian Woodland and Shrubland\",\"Central Appalachian Floodplain
        - Forest Modifier\",\"Central Appalachian Riparian - Forest Modifier\",\"Central
        Interior and Appalachian Floodplain Systems\",\"Central Interior and Appalachian
        Riparian Systems\",\"Laurentian-Acadian Floodplain Systems\",\"Ozark-Ouachita
        Riparian\",\"South-Central Interior Large Floodplain\",\"South-Central Interior
        Large Floodplain - Forest Modifier\",\"South-Central Interior Small Stream
        and Riparian\",\"North-Central Interior and Appalachian Rich Swamp\",\"0\",\"0\",\"Laurentian-Acadian
        Swamp Systems\",\"North-Central Interior Wet Flatwoods\",\"0\",\"South-Central
        Interior / Upper Coastal Plain Wet Flatwoods\",\"0\",\"Southern Piedmont/Ridge
        and Valley Upland Depression Swamp\",\"Atlantic Coastal Plain Blackwater Stream
        Floodplain Forest - Forest Modifier\",\"Atlantic Coastal Plain Brownwater
        Stream Floodplain Forest\",\"Atlantic Coastal Plain Northern Tidal Wooded
        Swamp\",\"Atlantic Coastal Plain Small Blackwater River Floodplain Forest\",\"Atlantic
        Coastal Plain Small Brownwater River Floodplain Forest\",\"Atlantic Coastal
        Plain Southern Tidal Wooded Swamp\",\"East Gulf Coastal Plain Large River
        Floodplain Forest - Forest Modifier\",\"East Gulf Coastal Plain Small Stream
        and River Floodplain Forest\",\"East Gulf Coastal Plain Tidal Wooded Swamp\",\"0\",\"Southeastern
        Great Plains Riparian Forest\",\"Southeastern Great Plains Floodplain Forest\",\"Mississippi
        River Bottomland Depression\",\"Mississippi River Floodplain and Riparian
        Forest\",\"Mississippi River Low Floodplain (Bottomland) Forest\",\"Mississippi
        River Riparian Forest\",\"Red River Large Floodplain Forest\",\"Southern Coastal
        Plain Blackwater River Floodplain Forest\",\"Southern Piedmont Large Floodplain
        Forest - Forest Modifier\",\"Southern Piedmont Small Floodplain and Riparian
        Forest\",\"West Gulf Coastal Plain Large River Floodplain Forest\",\"West
        Gulf Coastal Plain Near-Coast Large River Swamp\",\"West Gulf Coastal Plain
        Small Stream and River Forest\",\"Atlantic Coastal Plain Streamhead Seepage
        Swamp -  Pocosin -  and Baygall\",\"Gulf and Atlantic Coastal Plain Swamp
        Systems\",\"Southern Coastal Plain Hydric Hammock\",\"Southern Coastal Plain
        Seepage Swamp and Baygall\",\"West Gulf Coastal Plain Seepage Swamp and Baygall\",\"Atlantic
        Coastal Plain Nonriverine Swamp and Wet Hardwood Forest  - Taxodium/Nyssa
        Modifier\",\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest
        - Oak Dominated Modifier\",\"East Gulf Coastal Plain Southern Loblolly-Hardwood
        Flatwoods\",\"Lower Mississippi River Bottomland Depressions - Forest Modifier\",\"Lower
        Mississippi River Flatwoods\",\"Northern Atlantic Coastal Plain Basin Swamp
        and Wet Hardwood Forest\",\"Southern Coastal Plain Nonriverine Basin Swamp\",\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\",\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\",\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\",\"West
        Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\",\"West Gulf Coastal
        Plain Pine-Hardwood Flatwoods\",\"Edwards Plateau Riparian\",\"Atlantic Coastal
        Plain Clay-Based Carolina Bay Forested Wetland\",\"Atlantic Coastal Plain
        Clay-Based Carolina Bay Herbaceous Wetland\",\"Atlantic Coastal Plain Southern
        Wet Pine Savanna and Flatwoods\",\"Central Atlantic Coastal Plain Wet Longleaf
        Pine Savanna and Flatwoods\",\"Central Florida Pine Flatwoods\",\"East Gulf
        Coastal Plain Near-Coast Pine Flatwoods\",\"East Gulf Coastal Plain Near-Coast
        Pine Flatwoods - Open Understory Modifier\",\"East Gulf Coastal Plain Near-Coast
        Pine Flatwoods - Scrub/Shrub Understory Modifier\",\"South Florida Pine Flatwoods\",\"Southern
        Coastal Plain Nonriverine Cypress Dome\",\"West Gulf Coastal Plain Wet Longleaf
        Pine Savanna and Flatwoods\",\"Columbia Basin Foothill Riparian Woodland and
        Shrubland\",\"Great Basin Foothill and Lower Montane Riparian Woodland and
        Shrubland\",\"0\",\"Northern Rocky Mountain Conifer Swamp\",\"Northern Rocky
        Mountain Lower Montane Riparian Woodland and Shrubland\",\"Rocky Mountain
        Lower Montane Riparian Woodland and Shrubland\",\"Rocky Mountain Montane Riparian
        Systems\",\"Rocky Mountain Subalpine-Montane Riparian Woodland\",\"North Pacific
        Hardwood-Conifer Swamp\",\"North Pacific Lowland Riparian Forest and Shrubland\",\"North
        Pacific Montane Riparian Woodland and Shrubland\",\"North Pacific Shrub Swamp\",\"California
        Central Valley Riparian Woodland and Shrubland\",\"Mediterranean California
        Foothill and Lower Montane Riparian Woodland\",\"Mediterranean California
        Serpentine Foothill and Lower Montane Riparian Woodland and Seep\",\"North
        American Warm Desert Lower Montane Riparian Woodland and Shrubland\",\"North
        American Warm Desert Riparian Systems\",\"North American Warm Desert Riparian
        Woodland and Shrubland\",\"Tamaulipan Floodplain\",\"Tamaulipan Riparian Systems\",\"Boreal
        Aspen-Birch Forest\",\"Boreal Jack Pine-Black Spruce Forest\",\"Boreal White
        Spruce-Fir-Hardwood Forest\",\"Boreal-Laurentian Conifer Acidic Swamp and
        Treed Poor Fen\",\"Eastern Boreal Floodplain\",\"South Florida Shell Hash
        Beach\",\"Southeast Florida Beach\",\"Southwest Florida Beach\",\"South Florida
        Everglades Sawgrass Marsh\",\"South Florida Freshwater Slough and Gator Hole\",\"South
        Florida Wet Marl Prairie\",\"California Maritime Chaparral\",\"California
        Mesic Chaparral\",\"California Xeric Serpentine Chaparral\",\"Klamath-Siskiyou
        Xeromorphic Serpentine Savanna and Chaparral\",\"Mediterranean California
        Mesic Serpentine Woodland and Chaparral\",\"Northern and Central California
        Dry-Mesic Chaparral\",\"Southern California Dry-Mesic Chaparral\",\"Southern
        California Coastal Scrub\",\"California Central Valley and Southern Coastal
        Grassland\",\"California Mesic Serpentine Grassland\",\"Columbia Basin Foothill
        and Canyon Dry Grassland\",\"Columbia Basin Palouse Prairie\",\"North Pacific
        Alpine and Subalpine Dry Grassland\",\"North Pacific Montane Grassland\",\"North
        Pacific Montane Shrubland\",\"Northern Rocky Mountain Lower Montane, Foothill
        and Valley Grassland\",\"Northern Rocky Mountain Montane-Foothill Deciduous
        Shrubland\",\"Northern Rocky Mountain Subalpine Deciduous Shrubland\",\"Northern
        Rocky Mountain Subalpine-Upper Montane Grassland\",\"Southern Rocky Mountain
        Montane-Subalpine Grassland\",\"Rocky Mountain Gambel Oak-Mixed Montane Shrubland\",\"Rocky
        Mountain Lower Montane-Foothill Shrubland\",\"California Northern Coastal
        Grassland\",\"North Pacific Herbaceous Bald and Bluff\",\"North Pacific Hypermaritime
        Shrub and Herbaceous Headland\",\"Willamette Valley Upland Prairie and Savanna\",\"Mediterranean
        California Subalpine Meadow\",\"Rocky Mountain Subalpine-Montane Mesic Meadow\",\"Central
        Mixedgrass Prairie\",\"Northwestern Great Plains Mixedgrass Prairie\",\"Western
        Great Plains Foothill and Piedmont Grassland\",\"Western Great Plains Tallgrass
        Prairie\",\"Western Great Plains Sand Prairie\",\"Western Great Plains Sandhill
        Steppe\",\"Western Great Plains Mesquite Woodland and Shrubland\",\"Western
        Great Plains Shortgrass Prairie\",\"Arkansas Valley Prairie and Woodland\",\"Central
        Tallgrass Prairie\",\"North-Central Interior Oak Savanna\",\"North-Central
        Interior Sand and Gravel Tallgrass Prairie\",\"North-Central Oak Barrens\",\"Northern
        Tallgrass Prairie\",\"Southeastern Great Plains Tallgrass Prairie\",\"Texas
        Blackland Tallgrass Prairie\",\"Texas-Louisiana Coastal Prairie\",\"Central
        Appalachian Pine-Oak Rocky Woodland\",\"Southern Appalachian Grass and Shrub
        Bald\",\"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier\",\"Southern
        Appalachian Grass and Shrub Bald - Shrub Modifier\",\"Central Appalachian
        Alkaline Glade and Woodland\",\"Central Interior Highlands Calcareous Glade
        and Barrens\",\"Central Interior Highlands Dry Acidic Glade and Barrens\",\"Cumberland
        Sandstone Glade and Barrens\",\"Great Lakes Alvar\",\"Nashville Basin Limestone
        Glade\",\"Southern Ridge and Valley / Cumberland Dry Calcareous Forest\",\"Southern
        Piedmont Glade and Barrens\",\"East Gulf Coastal Plain Black Belt Calcareous
        Prairie and Woodland - Herbaceous Modifier\",\"East Gulf Coastal Plain Jackson
        Prairie and Woodland\",\"Eastern Highland Rim Prairie and Barrens - Dry Modifier\",\"Coahuilan
        Chaparral\",\"Madrean Oriental Chaparral\",\"Mogollon Chaparral\",\"Sonora-Mojave
        Semi-Desert Chaparral\",\"California Montane Woodland and Chaparral\",\"Great
        Basin Semi-Desert Chaparral\",\"Florida Dry Prairie\",\"Florida Peninsula
        Inland Scrub\",\"West Gulf Coastal Plain Catahoula Barrens\",\"West Gulf Coastal
        Plain Nepheline Syenite Glade\",\"East Gulf Coastal Plain Jackson Plain Dry
        Flatwoods - Open Understory Modifier\",\"West Gulf Coastal Plain Northern
        Calcareous Prairie\",\"West Gulf Coastal Plain Southern Calcareous Prairie\",\"Acadian-Appalachian
        Subalpine Woodland and Heath-Krummholz\",\"Atlantic and Gulf Coastal Plain
        Interdunal Wetland\",\"Atlantic Coastal Plain Southern Dune and Maritime Grassland\",\"Central
        and Upper Texas Coast Dune and Coastal Grassland\",\"East Gulf Coastal Plain
        Dune and Coastal Grassland\",\"Great Lakes Dune\",\"Northern Atlantic Coastal
        Plain Dune and Swale\",\"Northern Atlantic Coastal Plain Heathland and Grassland\",\"South
        Texas Dune and Coastal Grassland\",\"South Texas Sand Sheet Grassland\",\"Southwest
        Florida Dune and Coastal Grassland\",\"North Pacific Coastal Cliff and Bluff\",\"North
        Pacific Maritime Coastal Sand Dune and Strand\",\"Northern California Coastal
        Scrub\",\"Mediterranean California Coastal Bluff\",\"Mediterranean California
        Northern Coastal Dune\",\"Mediterranean California Southern Coastal Dune\",\"Atlantic
        Coastal Plain Northern Sandy Beach\",\"Atlantic Coastal Plain Sea Island Beach\",\"Atlantic
        Coastal Plain Southern Beach\",\"Florida Panhandle Beach Vegetation\",\"Louisiana
        Beach\",\"Northern Atlantic Coastal Plain Sandy Beach\",\"Texas Coastal Bend
        Beach\",\"Upper Texas Coast Beach\",\"0\",\"Mediterranean California Serpentine
        Fen\",\"Mediterranean California Subalpine-Montane Fen\",\"North Pacific Bog
        and Fen\",\"Rocky Mountain Subalpine-Montane Fen\",\"Atlantic Coastal Plain
        Peatland Pocosin\",\"Southern and Central Appalachian Bog and Fen\",\"Atlantic
        Coastal Plain Central Fresh-Oligohaline Tidal Marsh\",\"Atlantic Coastal Plain
        Embayed Region Tidal Freshwater Marsh\",\"Atlantic Coastal Plain Northern
        Fresh and Oligohaline Tidal Marsh\",\"Florida Big Bend Fresh-Oligohaline Tidal
        Marsh\",\"Atlantic Coastal Plain Depression Pondshore\",\"Atlantic Coastal
        Plain Large Natural Lakeshore\",\"Central Florida Herbaceous Pondshore\",\"Central
        Florida Herbaceous Seep\",\"East Gulf Coastal Plain Savanna and Wet Prairie\",\"East
        Gulf Coastal Plain Depression Pondshore\",\"Floridian Highlands Freshwater
        Marsh\",\"Southern Coastal Plain Herbaceous Seepage Bog\",\"Southern Coastal
        Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\",\"Southern Coastal
        Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\",\"Texas-Louisiana
        Coastal Prairie Slough\",\"Central Interior and Appalachian Shrub-Herbaceous
        Wetland Systems\",\"Great Lakes Coastal Marsh Systems\",\"0\",\"0\",\"Laurentian-Acadian
        Shrub-Herbaceous Wetland Systems\",\"0\",\"Eastern Great Plains Wet Meadow,
        Prairie  and Marsh\",\"Great Lakes Wet-Mesic Lakeplain Prairie\",\"Great Plains
        Prairie Pothole\",\"Western Great Plains Closed Depression Wetland\",\"Western
        Great Plains Depressional Wetland Systems\",\"Western Great Plains Open Freshwater
        Depression Wetland\",\"Cumberland Riverscour\",\"Inter-Mountain Basins Interdunal
        Swale Wetland\",\"North Pacific Avalanche Chute Shrubland\",\"North Pacific
        Intertidal Freshwater Wetland\",\"Temperate Pacific Freshwater Emergent Marsh\",\"Temperate
        Pacific Freshwater Mudflat\",\"Columbia Plateau Vernal Pool\",\"Northern California
        Claypan Vernal Pool\",\"Northern Rocky Mountain Wooded Vernal Pool\",\"Columbia
        Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe\",\"Rocky Mountain
        Alpine-Montane Wet Meadow\",\"Rocky Mountain Subalpine-Montane Riparian Shrubland\",\"Temperate
        Pacific Montane Wet Meadow\",\"Willamette Valley Wet Prairie\",\"Chihuahuan-Sonoran
        Desert Bottomland and Swale Grassland\",\"North American Arid West Emergent
        Marsh\",\"North American Warm Desert Riparian Mesquite Bosque\",\"Western
        Great Plains Saline Depression Wetland\",\"Acadian Salt Marsh and Estuary
        Systems\",\"Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh\",\"Atlantic
        Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\",\"Atlantic Coastal
        Plain Indian River Lagoon Tidal Marsh\",\"Atlantic Coastal Plain Northern
        Tidal Salt Marsh\",\"Florida Big Bend Salt-Brackish Tidal Marsh\",\"Gulf and
        Atlantic Coastal Plain Tidal Marsh Systems\",\"Mississippi Sound Salt and
        Brackish Tidal Marsh\",\"Texas Saline Coastal Prairie\",\"Temperate Pacific
        Tidal Salt and Brackish Marsh\",\"Inter-Mountain Basins Alkaline Closed Depression\",\"Inter-Mountain
        Basins Greasewood Flat\",\"Inter-Mountain Basins Playa\",\"North American
        Warm Desert Playa\",\"Apacherian-Chihuahuan Mesquite Upland Scrub\",\"Apacherian-Chihuahuan
        Semi-Desert Grassland and Steppe\",\"Chihuahuan Creosotebush, Mixed Desert
        and Thorn Scrub\",\"Chihuahuan Gypsophilous Grassland and Steppe\",\"Chihuahuan
        Loamy Plains Desert Grassland\",\"Chihuahuan Mixed Desert and Thorn Scrub\",\"Chihuahuan
        Sandy Plains Semi-Desert Grassland\",\"Chihuahuan Stabilized Coppice Dune
        and Sand Flat Scrub\",\"Chihuahuan Succulent Desert Scrub\",\"Madrean Juniper
        Savanna\",\"Mojave Mid-Elevation Mixed Desert Scrub\",\"North American Warm
        Desert Active and Stabilized Dune\",\"Sonora-Mojave Creosotebush-White Bursage
        Desert Scrub\",\"Sonoran Mid-Elevation Desert Scrub\",\"Sonoran Paloverde-Mixed
        Cacti Desert Scrub\",\"Chihuahuan Mixed Salt Desert Scrub\",\"Sonora-Mojave
        Mixed Salt Desert Scrub\",\"North American Warm Desert Wash\",\"South Texas
        Lomas\",\"Tamaulipan Calcareous Thornscrub\",\"Tamaulipan Clay Grassland\",\"Tamaulipan
        Mesquite Upland Scrub\",\"Tamaulipan Mixed Deciduous Thornscrub\",\"Tamaulipan
        Savanna Grassland\",\"Inter-Mountain Basins Mat Saltbush Shrubland\",\"Inter-Mountain
        Basins Mixed Salt Desert Scrub\",\"Inter-Mountain Basins Wash\",\"Columbia
        Plateau Steppe and Grassland\",\"Great Basin Xeric Mixed Sagebrush Shrubland\",\"Inter-Mountain
        Basins Big Sagebrush Shrubland\",\"Inter-Mountain Basins Big Sagebrush Steppe\",\"Inter-Mountain
        Basins Montane Sagebrush Steppe\",\"Colorado Plateau Mixed Low Sagebrush Shrubland\",\"Columbia
        Plateau Low Sagebrush Steppe\",\"Columbia Plateau Scabland Shrubland\",\"Wyoming
        Basins Dwarf Sagebrush Shrubland and Steppe\",\"Colorado Plateau Blackbrush-Mormon-tea
        Shrubland\",\"Inter-Mountain Basins Semi-Desert Grassland\",\"Inter-Mountain
        Basins Semi-Desert Shrub Steppe\",\"Southern Colorado Plateau Sand Shrubland\",\"Acadian-Appalachian
        Alpine Tundra\",\"Rocky Mountain Alpine Dwarf-Shrubland\",\"Rocky Mountain
        Alpine Fell-Field\",\"Rocky Mountain Alpine Turf\",\"Mediterranean California
        Alpine Dry Tundra\",\"Mediterranean California Alpine Fell-Field\",\"North
        Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow\",\"Rocky
        Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\",\"Temperate Pacific
        Intertidal Mudflat\",\"Mediterranean California Eelgrass Bed\",\"North Pacific
        Maritime Eelgrass Bed\",\"South-Central Interior Large Floodplain - Herbaceous
        Modifier\",\"East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous
        Modifier\",\"Temperate Pacific Freshwater Aquatic Bed\",\"Central California
        Coast Ranges Cliff and Canyon\",\"Mediterranean California Serpentine Barrens\",\"Southern
        California Coast Ranges Cliff and Canyon\",\"Central Interior Acidic Cliff
        and Talus\",\"Central Interior Calcareous Cliff and Talus\",\"East Gulf Coastal
        Plain Dry Chalk Bluff\",\"North-Central Appalachian Acidic Cliff and Talus\",\"North-Central
        Appalachian Circumneutral Cliff and Talus\",\"Southern Appalachian Montane
        Cliff\",\"Southern Interior Acid Cliff\",\"Southern Interior Calcareous Cliff\",\"Southern
        Piedmont Cliff\",\"Southern Appalachian Granitic Dome\",\"Southern Appalachian
        Rocky Summit\",\"Southern Piedmont Granite Flatrock\",\"Rocky Mountain Cliff,
        Canyon and Massive Bedrock\",\"Klamath-Siskiyou Cliff and Outcrop\",\"North
        Pacific Montane Massive Bedrock, Cliff and Talus\",\"North Pacific Serpentine
        Barren\",\"North Pacific Active Volcanic Rock and Cinder Land\",\"Sierra Nevada
        Cliff and Canyon\",\"Western Great Plains Badland\",\"Southwestern Great Plains
        Canyon\",\"Western Great Plains Cliff and Outcrop\",\"North American Warm
        Desert Badland\",\"North American Warm Desert Bedrock Cliff and Outcrop\",\"North
        American Warm Desert Pavement\",\"North American Warm Desert Volcanic Rockland\",\"Colorado
        Plateau Mixed Bedrock Canyon and Tableland\",\"Columbia Plateau Ash and Tuff
        Badland\",\"Geysers and Hot Springs\",\"Inter-Mountain Basins Active and Stabilized
        Dune\",\"Inter-Mountain Basins Cliff and Canyon\",\"Inter-Mountain Basins
        Shale Badland\",\"Inter-Mountain Basins Volcanic Rock and Cinder Land\",\"Rocky
        Mountain Alpine Bedrock and Scree\",\"Mediterranean California Alpine Bedrock
        and Scree\",\"North Pacific Alpine and Subalpine Bedrock and Scree\",\"Unconsolidated
        Shore\",\"Undifferentiated Barren Land\",\"North American Alpine Ice Field\",\"Orchards
        Vineyards and Other High Structure Agriculture\",\"Cultivated Cropland\",\"Pasture/Hay\",\"Introduced
        Upland Vegetation - Annual Grassland\",\"Introduced Upland Vegetation - Perennial
        Grassland and Forbland\",\"Modified/Managed Southern Tall Grassland\",\"Introduced
        Upland Vegetation - Shrub\",\"Introduced Riparian and Wetland Vegetation\",\"Introduced
        Upland Vegetation - Treed\",\"0\",\"Disturbed, Non-specific\",\"Recently Logged
        Areas\",\"Harvested Forest - Grass/Forb Regeneration\",\"Harvested Forest-Shrub
        Regeneration\",\"Harvested Forest - Northwestern Conifer Regeneration\",\"Recently
        Burned\",\"Recently burned grassland\",\"Recently burned shrubland\",\"Recently
        burned forest\",\"Disturbed/Successional - Grass/Forb Regeneration\",\"Disturbed/Successional
        - Shrub Regeneration\",\"Disturbed/Successional - Recently Chained Pinyon-Juniper\",\"Open
        Water (Aquaculture)\",\"Open Water (Brackish/Salt)\",\"Open Water (Fresh)\",\"Quarries,
        Mines, Gravel Pits and Oil Wells\",\"Developed, Open Space\",\"Developed,
        Low Intensity\",\"Developed, Medium Intensity\",\"Developed, High Intensity\"]}]},\"description\":\"The
        [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5),
        based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states),
        are the foundation of the most detailed, consistent map of vegetation available
        for the United States.  These data facilitate planning and management for
        biological diversity on a regional and national scale.\\n\\nThis dataset includes
        the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover)
        component of the GAP/LANDFIRE project.\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"GeoTIFF
        data\",\"file:values\":[{\"values\":[0],\"summary\":\"0\"},{\"values\":[1],\"summary\":\"South
        Florida Bayhead Swamp\"},{\"values\":[2],\"summary\":\"South Florida Cypress
        Dome\"},{\"values\":[3],\"summary\":\"South Florida Dwarf Cypress Savanna\"},{\"values\":[4],\"summary\":\"South
        Florida Mangrove Swamp\"},{\"values\":[5],\"summary\":\"South Florida Hardwood
        Hammock\"},{\"values\":[6],\"summary\":\"Southeast Florida Coastal Strand
        and Maritime Hammock\"},{\"values\":[7],\"summary\":\"Southwest Florida Coastal
        Strand and Maritime Hammock\"},{\"values\":[8],\"summary\":\"South Florida
        Pine Rockland\"},{\"values\":[9],\"summary\":\"Atlantic Coastal Plain Fall-line
        Sandhills Longleaf Pine Woodland - Open Understory\"},{\"values\":[10],\"summary\":\"Atlantic
        Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\"},{\"values\":[11],\"summary\":\"Atlantic
        Coastal Plain Upland Longleaf Pine Woodland\"},{\"values\":[12],\"summary\":\"Atlantic
        Coastal Plain Xeric River Dune\"},{\"values\":[13],\"summary\":\"East Gulf
        Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier\"},{\"values\":[14],\"summary\":\"East
        Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier\"},{\"values\":[15],\"summary\":\"Florida
        Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier\"},{\"values\":[16],\"summary\":\"Florida
        Longleaf Pine Sandhill- Open Understory Modifier\"},{\"values\":[17],\"summary\":\"West
        Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\"},{\"values\":[18],\"summary\":\"Atlantic
        Coastal Plain Central Maritime Forest\"},{\"values\":[19],\"summary\":\"Atlantic
        Coastal Plain Southern Maritime Forest\"},{\"values\":[20],\"summary\":\"Central
        and South Texas Coastal Fringe Forest and Woodland\"},{\"values\":[21],\"summary\":\"East
        Gulf Coastal Plain Limestone Forest\"},{\"values\":[22],\"summary\":\"East
        Gulf Coastal Plain Maritime Forest\"},{\"values\":[23],\"summary\":\"East
        Gulf Coastal Plain Southern Loess Bluff Forest\"},{\"values\":[24],\"summary\":\"East
        Gulf Coastal Plain Southern Mesic Slope Forest\"},{\"values\":[25],\"summary\":\"Mississippi
        Delta Maritime Forest\"},{\"values\":[26],\"summary\":\"Southern Coastal Plain
        Dry Upland Hardwood Forest\"},{\"values\":[27],\"summary\":\"Southern Coastal
        Plain Oak Dome and Hammock\"},{\"values\":[28],\"summary\":\"West Gulf Coastal
        Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland\"},{\"values\":[29],\"summary\":\"West
        Gulf Coastal Plain Mesic Hardwood Forest\"},{\"values\":[30],\"summary\":\"East-Central
        Texas Plains Pine Forest and Woodland\"},{\"values\":[31],\"summary\":\"West
        Gulf Coastal Plain Pine-Hardwood Forest\"},{\"values\":[32],\"summary\":\"West
        Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland\"},{\"values\":[33],\"summary\":\"Atlantic
        Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier\"},{\"values\":[34],\"summary\":\"Deciduous
        Plantations\"},{\"values\":[35],\"summary\":\"East Gulf Coastal Plain Interior
        Upland Longleaf Pine Woodland - Loblolly Modifier\"},{\"values\":[36],\"summary\":\"East
        Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood
        Modifier\"},{\"values\":[37],\"summary\":\"East Gulf Coastal Plain Near-Coast
        Pine Flatwoods - Offsite Hardwood Modifier\"},{\"values\":[38],\"summary\":\"Evergreen
        Plantation or Managed Pine\"},{\"values\":[39],\"summary\":\"California Central
        Valley Mixed Oak Savanna\"},{\"values\":[40],\"summary\":\"California Coastal
        Closed-Cone Conifer Forest and Woodland\"},{\"values\":[41],\"summary\":\"California
        Coastal Live Oak Woodland and Savanna\"},{\"values\":[42],\"summary\":\"California
        Lower Montane Blue Oak-Foothill Pine Woodland and Savanna\"},{\"values\":[43],\"summary\":\"Central
        and Southern California Mixed Evergreen Woodland\"},{\"values\":[44],\"summary\":\"Mediterranean
        California Lower Montane Black Oak-Conifer Forest and Woodland\"},{\"values\":[45],\"summary\":\"Southern
        California Oak Woodland and Savanna\"},{\"values\":[46],\"summary\":\"Madrean
        Encinal\"},{\"values\":[47],\"summary\":\"Madrean Pinyon-Juniper Woodland\"},{\"values\":[48],\"summary\":\"Madrean
        Pine-Oak Forest and Woodland\"},{\"values\":[49],\"summary\":\"Madrean Upper
        Montane Conifer-Oak Forest and Woodland\"},{\"values\":[50],\"summary\":\"Edwards
        Plateau Dry-Mesic Slope Forest and Woodland\"},{\"values\":[51],\"summary\":\"Edwards
        Plateau Limestone Savanna and Woodland\"},{\"values\":[52],\"summary\":\"Edwards
        Plateau Mesic Canyon\"},{\"values\":[53],\"summary\":\"Llano Uplift Acidic
        Forest, Woodland and Glade\"},{\"values\":[54],\"summary\":\"East Cascades
        Oak-Ponderosa Pine Forest and Woodland\"},{\"values\":[55],\"summary\":\"Mediterranean
        California Mixed Evergreen Forest\"},{\"values\":[56],\"summary\":\"Mediterranean
        California Mixed Oak Woodland\"},{\"values\":[57],\"summary\":\"North Pacific
        Dry Douglas-fir-(Madrone) Forest and Woodland\"},{\"values\":[58],\"summary\":\"North
        Pacific Oak Woodland\"},{\"values\":[59],\"summary\":\"Edwards Plateau Limestone
        Shrubland\"},{\"values\":[60],\"summary\":\"Allegheny-Cumberland Dry Oak Forest
        and Woodland - Hardwood\"},{\"values\":[61],\"summary\":\"Allegheny-Cumberland
        Dry Oak Forest and Woodland - Pine Modifier\"},{\"values\":[62],\"summary\":\"Central
        and Southern Appalachian Montane Oak Forest\"},{\"values\":[63],\"summary\":\"Central
        and Southern Appalachian Northern Hardwood Forest\"},{\"values\":[64],\"summary\":\"Central
        Appalachian Oak and Pine Forest\"},{\"values\":[65],\"summary\":\"Crosstimbers
        Oak Forest and Woodland\"},{\"values\":[66],\"summary\":\"East Gulf Coastal
        Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier\"},{\"values\":[67],\"summary\":\"East
        Gulf Coastal Plain Northern Dry Upland Hardwood Forest\"},{\"values\":[68],\"summary\":\"East
        Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier\"},{\"values\":[69],\"summary\":\"East
        Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier\"},{\"values\":[70],\"summary\":\"East-Central
        Texas Plains Post Oak Savanna and Woodland\"},{\"values\":[71],\"summary\":\"Lower
        Mississippi River Dune Woodland and Forest\"},{\"values\":[72],\"summary\":\"Mississippi
        River Alluvial Plain Dry-Mesic Loess Slope Forest\"},{\"values\":[73],\"summary\":\"North-Central
        Interior Dry Oak Forest and Woodland\"},{\"values\":[74],\"summary\":\"North-Central
        Interior Dry-Mesic Oak Forest and Woodland\"},{\"values\":[75],\"summary\":\"Northeastern
        Interior Dry Oak Forest - Mixed Modifier\"},{\"values\":[76],\"summary\":\"Northeastern
        Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\"},{\"values\":[77],\"summary\":\"Northeastern
        Interior Dry Oak Forest-Hardwood Modifier\"},{\"values\":[78],\"summary\":\"Northeastern
        Interior Dry-Mesic Oak Forest\"},{\"values\":[79],\"summary\":\"Northern Atlantic
        Coastal Plain Dry Hardwood Forest\"},{\"values\":[80],\"summary\":\"Crowley's
        Ridge Sand Forest\"},{\"values\":[81],\"summary\":\"Ouachita Montane Oak Forest\"},{\"values\":[82],\"summary\":\"Ozark-Ouachita
        Dry Oak Woodland\"},{\"values\":[83],\"summary\":\"Ozark-Ouachita Dry-Mesic
        Oak Forest\"},{\"values\":[84],\"summary\":\"Southern and Central Appalachian
        Oak Forest\"},{\"values\":[85],\"summary\":\"Southern and Central Appalachian
        Oak Forest - Xeric\"},{\"values\":[86],\"summary\":\"Southern Interior Low
        Plateau Dry-Mesic Oak Forest\"},{\"values\":[87],\"summary\":\"Southern Ridge
        and Valley Dry Calcareous Forest\"},{\"values\":[88],\"summary\":\"Southern
        Ridge and Valley Dry Calcareous Forest - Pine modifier\"},{\"values\":[89],\"summary\":\"East
        Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\"},{\"values\":[90],\"summary\":\"Managed
        Tree Plantation\"},{\"values\":[91],\"summary\":\"Ruderal forest\"},{\"values\":[92],\"summary\":\"Southern
        Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier\"},{\"values\":[93],\"summary\":\"Acadian
        Low-Elevation Spruce-Fir-Hardwood Forest\"},{\"values\":[94],\"summary\":\"Acadian-Appalachian
        Montane Spruce-Fir Forest\"},{\"values\":[95],\"summary\":\"Appalachian Hemlock-Hardwood
        Forest\"},{\"values\":[96],\"summary\":\"Central and Southern Appalachian
        Spruce-Fir Forest\"},{\"values\":[97],\"summary\":\"0\"},{\"values\":[98],\"summary\":\"Laurentian-Acadian
        Northern Hardwoods Forest\"},{\"values\":[99],\"summary\":\"Laurentian-Acadian
        Northern Pine-(Oak) Forest\"},{\"values\":[100],\"summary\":\"Laurentian-Acadian
        Pine-Hemlock-Hardwood Forest\"},{\"values\":[101],\"summary\":\"Paleozoic
        Plateau Bluff and Talus\"},{\"values\":[102],\"summary\":\"Southern Appalachian
        Northern Hardwood Forest\"},{\"values\":[103],\"summary\":\"Atlantic Coastal
        Plain Dry and Dry-Mesic Oak Forest\"},{\"values\":[104],\"summary\":\"Atlantic
        Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood\"},{\"values\":[105],\"summary\":\"East
        Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier\"},{\"values\":[106],\"summary\":\"East
        Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier\"},{\"values\":[107],\"summary\":\"Ozark-Ouachita
        Shortleaf Pine-Bluestem Woodland\"},{\"values\":[108],\"summary\":\"Ozark-Ouachita
        Shortleaf Pine-Oak Forest and Woodland\"},{\"values\":[109],\"summary\":\"Southeastern
        Interior Longleaf Pine Woodland\"},{\"values\":[110],\"summary\":\"Southern
        Appalachian Low Mountain Pine Forest\"},{\"values\":[111],\"summary\":\"Southern
        Piedmont Dry Oak-(Pine) Forest\"},{\"values\":[112],\"summary\":\"Southern
        Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier\"},{\"values\":[113],\"summary\":\"Southern
        Piedmont Dry Oak-(Pine) Forest - Mixed Modifier\"},{\"values\":[114],\"summary\":\"Southern
        Piedmont Dry Oak-Heath Forest - Mixed Modifier\"},{\"values\":[115],\"summary\":\"Eastern
        Great Plains Tallgrass Aspen Parkland\"},{\"values\":[116],\"summary\":\"Northwestern
        Great Plains Aspen Forest and Parkland\"},{\"values\":[117],\"summary\":\"Northwestern
        Great Plains Shrubland\"},{\"values\":[118],\"summary\":\"Western Great Plains
        Dry Bur Oak Forest and Woodland\"},{\"values\":[119],\"summary\":\"Western
        Great Plains Wooded Draw and Ravine\"},{\"values\":[120],\"summary\":\"Southern
        Atlantic Coastal Plain Mesic Hardwood Forest\"},{\"values\":[121],\"summary\":\"East
        Gulf Coastal Plain Northern Loess Bluff Forest\"},{\"values\":[122],\"summary\":\"East
        Gulf Coastal Plain Northern Mesic Hardwood Forest\"},{\"values\":[123],\"summary\":\"North-Central
        Interior Beech-Maple Forest\"},{\"values\":[124],\"summary\":\"North-Central
        Interior Maple-Basswood Forest\"},{\"values\":[125],\"summary\":\"Ozark-Ouachita
        Mesic Hardwood Forest\"},{\"values\":[126],\"summary\":\"South-Central Interior
        Mesophytic Forest\"},{\"values\":[127],\"summary\":\"Southern and Central
        Appalachian Cove Forest\"},{\"values\":[128],\"summary\":\"Crowley's Ridge
        Mesic Loess Slope Forest\"},{\"values\":[129],\"summary\":\"Southern Piedmont
        Mesic Forest\"},{\"values\":[130],\"summary\":\"Appalachian Shale Barrens\"},{\"values\":[131],\"summary\":\"Atlantic
        Coastal Plain Northern Maritime Forest\"},{\"values\":[132],\"summary\":\"Laurentian
        Pine-Oak Barrens\"},{\"values\":[133],\"summary\":\"Northeastern Interior
        Pine Barrens\"},{\"values\":[134],\"summary\":\"Northern Atlantic Coastal
        Plain Pitch Pine Barrens\"},{\"values\":[135],\"summary\":\"Southern Appalachian
        Montane Pine Forest and Woodland\"},{\"values\":[136],\"summary\":\"East Cascades
        Mesic Montane Mixed-Conifer Forest and Woodland\"},{\"values\":[137],\"summary\":\"Middle
        Rocky Mountain Montane Douglas-fir Forest and Woodland\"},{\"values\":[138],\"summary\":\"Northern
        Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest\"},{\"values\":[139],\"summary\":\"Northern
        Rocky Mountain Foothill Conifer Wooded Steppe\"},{\"values\":[140],\"summary\":\"Northern
        Rocky Mountain Mesic Montane Mixed Conifer Forest\"},{\"values\":[141],\"summary\":\"Northern
        Rocky Mountain Ponderosa Pine Woodland and Savanna\"},{\"values\":[142],\"summary\":\"Northern
        Rocky Mountain Western Larch Savanna\"},{\"values\":[143],\"summary\":\"Northwestern
        Great Plains - Black Hills Ponderosa Pine Woodland and Savanna\"},{\"values\":[144],\"summary\":\"Rocky
        Mountain Foothill Limber Pine-Juniper Woodland\"},{\"values\":[145],\"summary\":\"Inter-Mountain
        Basins Aspen-Mixed Conifer Forest and Woodland\"},{\"values\":[146],\"summary\":\"Inter-Mountain
        Basins Subalpine Limber-Bristlecone Pine Woodland\"},{\"values\":[147],\"summary\":\"Northern
        Rocky Mountain Subalpine Woodland and Parkland\"},{\"values\":[148],\"summary\":\"Rocky
        Mountain Aspen Forest and Woodland\"},{\"values\":[149],\"summary\":\"Rocky
        Mountain Lodgepole Pine Forest\"},{\"values\":[150],\"summary\":\"Rocky Mountain
        Poor-Site Lodgepole Pine Forest\"},{\"values\":[151],\"summary\":\"Rocky Mountain
        Subalpine Dry-Mesic Spruce-Fir Forest and Woodland\"},{\"values\":[152],\"summary\":\"Rocky
        Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\"},{\"values\":[153],\"summary\":\"Rocky
        Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\"},{\"values\":[154],\"summary\":\"Rocky
        Mountain Bigtooth Maple Ravine Woodland\"},{\"values\":[155],\"summary\":\"Southern
        Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland\"},{\"values\":[156],\"summary\":\"Southern
        Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland\"},{\"values\":[157],\"summary\":\"Southern
        Rocky Mountain Ponderosa Pine Savanna\"},{\"values\":[158],\"summary\":\"Southern
        Rocky Mountain Ponderosa Pine Woodland\"},{\"values\":[159],\"summary\":\"California
        Montane Jeffrey Pine-(Ponderosa Pine) Woodland\"},{\"values\":[160],\"summary\":\"Klamath-Siskiyou
        Lower Montane Serpentine Mixed Conifer Woodland\"},{\"values\":[161],\"summary\":\"Klamath-Siskiyou
        Upper Montane Serpentine Mixed Conifer Woodland\"},{\"values\":[162],\"summary\":\"Mediterranean
        California Dry-Mesic Mixed Conifer Forest and Woodland\"},{\"values\":[163],\"summary\":\"Mediterranean
        California Mesic Mixed Conifer Forest and Woodland\"},{\"values\":[164],\"summary\":\"Sierran-Intermontane
        Desert Western White Pine-White Fir Woodland\"},{\"values\":[165],\"summary\":\"California
        Coastal Redwood Forest\"},{\"values\":[166],\"summary\":\"North Pacific Broadleaf
        Landslide Forest and Shrubland\"},{\"values\":[167],\"summary\":\"North Pacific
        Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest\"},{\"values\":[168],\"summary\":\"North
        Pacific Hypermaritime Sitka Spruce Forest\"},{\"values\":[169],\"summary\":\"North
        Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\"},{\"values\":[170],\"summary\":\"North
        Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland\"},{\"values\":[171],\"summary\":\"North
        Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest\"},{\"values\":[172],\"summary\":\"North
        Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest\"},{\"values\":[173],\"summary\":\"North
        Pacific Mesic Western Hemlock-Silver Fir Forest\"},{\"values\":[174],\"summary\":\"North
        Pacific Wooded Volcanic Flowage\"},{\"values\":[175],\"summary\":\"Mediterranean
        California Red Fir Forest\"},{\"values\":[176],\"summary\":\"Mediterranean
        California Subalpine Woodland\"},{\"values\":[177],\"summary\":\"North Pacific
        Maritime Mesic Subalpine Parkland\"},{\"values\":[178],\"summary\":\"North
        Pacific Mountain Hemlock Forest\"},{\"values\":[179],\"summary\":\"Northern
        California Mesic Subalpine Woodland\"},{\"values\":[180],\"summary\":\"Northern
        Pacific Mesic Subalpine Woodland\"},{\"values\":[181],\"summary\":\"Sierra
        Nevada Subalpine Lodgepole Pine Forest and Woodland\"},{\"values\":[182],\"summary\":\"Columbia
        Plateau Western Juniper Woodland and Savanna\"},{\"values\":[183],\"summary\":\"Great
        Basin Pinyon-Juniper Woodland\"},{\"values\":[184],\"summary\":\"Inter-Mountain
        Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\"},{\"values\":[185],\"summary\":\"Inter-Mountain
        Basins Juniper Savanna\"},{\"values\":[186],\"summary\":\"Colorado Plateau
        Pinyon-Juniper Shrubland\"},{\"values\":[187],\"summary\":\"Colorado Plateau
        Pinyon-Juniper Woodland\"},{\"values\":[188],\"summary\":\"Southern Rocky
        Mountain Juniper Woodland and Savanna\"},{\"values\":[189],\"summary\":\"Southern
        Rocky Mountain Pinyon-Juniper Woodland\"},{\"values\":[190],\"summary\":\"Northwestern
        Great Plains Floodplain\"},{\"values\":[191],\"summary\":\"Northwestern Great
        Plains Riparian\"},{\"values\":[192],\"summary\":\"Western Great Plains Floodplain\"},{\"values\":[193],\"summary\":\"Western
        Great Plains Floodplain Systems\"},{\"values\":[194],\"summary\":\"Western
        Great Plains Riparian Woodland and Shrubland\"},{\"values\":[195],\"summary\":\"Central
        Appalachian Floodplain - Forest Modifier\"},{\"values\":[196],\"summary\":\"Central
        Appalachian Riparian - Forest Modifier\"},{\"values\":[197],\"summary\":\"Central
        Interior and Appalachian Floodplain Systems\"},{\"values\":[198],\"summary\":\"Central
        Interior and Appalachian Riparian Systems\"},{\"values\":[199],\"summary\":\"Laurentian-Acadian
        Floodplain Systems\"},{\"values\":[200],\"summary\":\"Ozark-Ouachita Riparian\"},{\"values\":[201],\"summary\":\"South-Central
        Interior Large Floodplain\"},{\"values\":[202],\"summary\":\"South-Central
        Interior Large Floodplain - Forest Modifier\"},{\"values\":[203],\"summary\":\"South-Central
        Interior Small Stream and Riparian\"},{\"values\":[204],\"summary\":\"North-Central
        Interior and Appalachian Rich Swamp\"},{\"values\":[205],\"summary\":\"0\"},{\"values\":[206],\"summary\":\"0\"},{\"values\":[207],\"summary\":\"Laurentian-Acadian
        Swamp Systems\"},{\"values\":[208],\"summary\":\"North-Central Interior Wet
        Flatwoods\"},{\"values\":[209],\"summary\":\"0\"},{\"values\":[210],\"summary\":\"South-Central
        Interior / Upper Coastal Plain Wet Flatwoods\"},{\"values\":[211],\"summary\":\"0\"},{\"values\":[212],\"summary\":\"Southern
        Piedmont/Ridge and Valley Upland Depression Swamp\"},{\"values\":[213],\"summary\":\"Atlantic
        Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier\"},{\"values\":[214],\"summary\":\"Atlantic
        Coastal Plain Brownwater Stream Floodplain Forest\"},{\"values\":[215],\"summary\":\"Atlantic
        Coastal Plain Northern Tidal Wooded Swamp\"},{\"values\":[216],\"summary\":\"Atlantic
        Coastal Plain Small Blackwater River Floodplain Forest\"},{\"values\":[217],\"summary\":\"Atlantic
        Coastal Plain Small Brownwater River Floodplain Forest\"},{\"values\":[218],\"summary\":\"Atlantic
        Coastal Plain Southern Tidal Wooded Swamp\"},{\"values\":[219],\"summary\":\"East
        Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier\"},{\"values\":[220],\"summary\":\"East
        Gulf Coastal Plain Small Stream and River Floodplain Forest\"},{\"values\":[221],\"summary\":\"East
        Gulf Coastal Plain Tidal Wooded Swamp\"},{\"values\":[222],\"summary\":\"0\"},{\"values\":[223],\"summary\":\"Southeastern
        Great Plains Riparian Forest\"},{\"values\":[224],\"summary\":\"Southeastern
        Great Plains Floodplain Forest\"},{\"values\":[225],\"summary\":\"Mississippi
        River Bottomland Depression\"},{\"values\":[226],\"summary\":\"Mississippi
        River Floodplain and Riparian Forest\"},{\"values\":[227],\"summary\":\"Mississippi
        River Low Floodplain (Bottomland) Forest\"},{\"values\":[228],\"summary\":\"Mississippi
        River Riparian Forest\"},{\"values\":[229],\"summary\":\"Red River Large Floodplain
        Forest\"},{\"values\":[230],\"summary\":\"Southern Coastal Plain Blackwater
        River Floodplain Forest\"},{\"values\":[231],\"summary\":\"Southern Piedmont
        Large Floodplain Forest - Forest Modifier\"},{\"values\":[232],\"summary\":\"Southern
        Piedmont Small Floodplain and Riparian Forest\"},{\"values\":[233],\"summary\":\"West
        Gulf Coastal Plain Large River Floodplain Forest\"},{\"values\":[234],\"summary\":\"West
        Gulf Coastal Plain Near-Coast Large River Swamp\"},{\"values\":[235],\"summary\":\"West
        Gulf Coastal Plain Small Stream and River Forest\"},{\"values\":[236],\"summary\":\"Atlantic
        Coastal Plain Streamhead Seepage Swamp -  Pocosin -  and Baygall\"},{\"values\":[237],\"summary\":\"Gulf
        and Atlantic Coastal Plain Swamp Systems\"},{\"values\":[238],\"summary\":\"Southern
        Coastal Plain Hydric Hammock\"},{\"values\":[239],\"summary\":\"Southern Coastal
        Plain Seepage Swamp and Baygall\"},{\"values\":[240],\"summary\":\"West Gulf
        Coastal Plain Seepage Swamp and Baygall\"},{\"values\":[241],\"summary\":\"Atlantic
        Coastal Plain Nonriverine Swamp and Wet Hardwood Forest  - Taxodium/Nyssa
        Modifier\"},{\"values\":[242],\"summary\":\"Atlantic Coastal Plain Nonriverine
        Swamp and Wet Hardwood Forest - Oak Dominated Modifier\"},{\"values\":[243],\"summary\":\"East
        Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods\"},{\"values\":[244],\"summary\":\"Lower
        Mississippi River Bottomland Depressions - Forest Modifier\"},{\"values\":[245],\"summary\":\"Lower
        Mississippi River Flatwoods\"},{\"values\":[246],\"summary\":\"Northern Atlantic
        Coastal Plain Basin Swamp and Wet Hardwood Forest\"},{\"values\":[247],\"summary\":\"Southern
        Coastal Plain Nonriverine Basin Swamp\"},{\"values\":[248],\"summary\":\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\"},{\"values\":[249],\"summary\":\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\"},{\"values\":[250],\"summary\":\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\"},{\"values\":[251],\"summary\":\"West
        Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\"},{\"values\":[252],\"summary\":\"West
        Gulf Coastal Plain Pine-Hardwood Flatwoods\"},{\"values\":[253],\"summary\":\"Edwards
        Plateau Riparian\"},{\"values\":[254],\"summary\":\"Atlantic Coastal Plain
        Clay-Based Carolina Bay Forested Wetland\"},{\"values\":[255],\"summary\":\"Atlantic
        Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland\"},{\"values\":[256],\"summary\":\"Atlantic
        Coastal Plain Southern Wet Pine Savanna and Flatwoods\"},{\"values\":[257],\"summary\":\"Central
        Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\"},{\"values\":[258],\"summary\":\"Central
        Florida Pine Flatwoods\"},{\"values\":[259],\"summary\":\"East Gulf Coastal
        Plain Near-Coast Pine Flatwoods\"},{\"values\":[260],\"summary\":\"East Gulf
        Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier\"},{\"values\":[261],\"summary\":\"East
        Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier\"},{\"values\":[262],\"summary\":\"South
        Florida Pine Flatwoods\"},{\"values\":[263],\"summary\":\"Southern Coastal
        Plain Nonriverine Cypress Dome\"},{\"values\":[264],\"summary\":\"West Gulf
        Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\"},{\"values\":[265],\"summary\":\"Columbia
        Basin Foothill Riparian Woodland and Shrubland\"},{\"values\":[266],\"summary\":\"Great
        Basin Foothill and Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[267],\"summary\":\"0\"},{\"values\":[268],\"summary\":\"Northern
        Rocky Mountain Conifer Swamp\"},{\"values\":[269],\"summary\":\"Northern Rocky
        Mountain Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[270],\"summary\":\"Rocky
        Mountain Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[271],\"summary\":\"Rocky
        Mountain Montane Riparian Systems\"},{\"values\":[272],\"summary\":\"Rocky
        Mountain Subalpine-Montane Riparian Woodland\"},{\"values\":[273],\"summary\":\"North
        Pacific Hardwood-Conifer Swamp\"},{\"values\":[274],\"summary\":\"North Pacific
        Lowland Riparian Forest and Shrubland\"},{\"values\":[275],\"summary\":\"North
        Pacific Montane Riparian Woodland and Shrubland\"},{\"values\":[276],\"summary\":\"North
        Pacific Shrub Swamp\"},{\"values\":[277],\"summary\":\"California Central
        Valley Riparian Woodland and Shrubland\"},{\"values\":[278],\"summary\":\"Mediterranean
        California Foothill and Lower Montane Riparian Woodland\"},{\"values\":[279],\"summary\":\"Mediterranean
        California Serpentine Foothill and Lower Montane Riparian Woodland and Seep\"},{\"values\":[280],\"summary\":\"North
        American Warm Desert Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[281],\"summary\":\"North
        American Warm Desert Riparian Systems\"},{\"values\":[282],\"summary\":\"North
        American Warm Desert Riparian Woodland and Shrubland\"},{\"values\":[283],\"summary\":\"Tamaulipan
        Floodplain\"},{\"values\":[284],\"summary\":\"Tamaulipan Riparian Systems\"},{\"values\":[285],\"summary\":\"Boreal
        Aspen-Birch Forest\"},{\"values\":[286],\"summary\":\"Boreal Jack Pine-Black
        Spruce Forest\"},{\"values\":[287],\"summary\":\"Boreal White Spruce-Fir-Hardwood
        Forest\"},{\"values\":[288],\"summary\":\"Boreal-Laurentian Conifer Acidic
        Swamp and Treed Poor Fen\"},{\"values\":[289],\"summary\":\"Eastern Boreal
        Floodplain\"},{\"values\":[290],\"summary\":\"South Florida Shell Hash Beach\"},{\"values\":[291],\"summary\":\"Southeast
        Florida Beach\"},{\"values\":[292],\"summary\":\"Southwest Florida Beach\"},{\"values\":[293],\"summary\":\"South
        Florida Everglades Sawgrass Marsh\"},{\"values\":[294],\"summary\":\"South
        Florida Freshwater Slough and Gator Hole\"},{\"values\":[295],\"summary\":\"South
        Florida Wet Marl Prairie\"},{\"values\":[296],\"summary\":\"California Maritime
        Chaparral\"},{\"values\":[297],\"summary\":\"California Mesic Chaparral\"},{\"values\":[298],\"summary\":\"California
        Xeric Serpentine Chaparral\"},{\"values\":[299],\"summary\":\"Klamath-Siskiyou
        Xeromorphic Serpentine Savanna and Chaparral\"},{\"values\":[300],\"summary\":\"Mediterranean
        California Mesic Serpentine Woodland and Chaparral\"},{\"values\":[301],\"summary\":\"Northern
        and Central California Dry-Mesic Chaparral\"},{\"values\":[302],\"summary\":\"Southern
        California Dry-Mesic Chaparral\"},{\"values\":[303],\"summary\":\"Southern
        California Coastal Scrub\"},{\"values\":[304],\"summary\":\"California Central
        Valley and Southern Coastal Grassland\"},{\"values\":[305],\"summary\":\"California
        Mesic Serpentine Grassland\"},{\"values\":[306],\"summary\":\"Columbia Basin
        Foothill and Canyon Dry Grassland\"},{\"values\":[307],\"summary\":\"Columbia
        Basin Palouse Prairie\"},{\"values\":[308],\"summary\":\"North Pacific Alpine
        and Subalpine Dry Grassland\"},{\"values\":[309],\"summary\":\"North Pacific
        Montane Grassland\"},{\"values\":[310],\"summary\":\"North Pacific Montane
        Shrubland\"},{\"values\":[311],\"summary\":\"Northern Rocky Mountain Lower
        Montane, Foothill and Valley Grassland\"},{\"values\":[312],\"summary\":\"Northern
        Rocky Mountain Montane-Foothill Deciduous Shrubland\"},{\"values\":[313],\"summary\":\"Northern
        Rocky Mountain Subalpine Deciduous Shrubland\"},{\"values\":[314],\"summary\":\"Northern
        Rocky Mountain Subalpine-Upper Montane Grassland\"},{\"values\":[315],\"summary\":\"Southern
        Rocky Mountain Montane-Subalpine Grassland\"},{\"values\":[316],\"summary\":\"Rocky
        Mountain Gambel Oak-Mixed Montane Shrubland\"},{\"values\":[317],\"summary\":\"Rocky
        Mountain Lower Montane-Foothill Shrubland\"},{\"values\":[318],\"summary\":\"California
        Northern Coastal Grassland\"},{\"values\":[319],\"summary\":\"North Pacific
        Herbaceous Bald and Bluff\"},{\"values\":[320],\"summary\":\"North Pacific
        Hypermaritime Shrub and Herbaceous Headland\"},{\"values\":[321],\"summary\":\"Willamette
        Valley Upland Prairie and Savanna\"},{\"values\":[322],\"summary\":\"Mediterranean
        California Subalpine Meadow\"},{\"values\":[323],\"summary\":\"Rocky Mountain
        Subalpine-Montane Mesic Meadow\"},{\"values\":[324],\"summary\":\"Central
        Mixedgrass Prairie\"},{\"values\":[325],\"summary\":\"Northwestern Great Plains
        Mixedgrass Prairie\"},{\"values\":[326],\"summary\":\"Western Great Plains
        Foothill and Piedmont Grassland\"},{\"values\":[327],\"summary\":\"Western
        Great Plains Tallgrass Prairie\"},{\"values\":[328],\"summary\":\"Western
        Great Plains Sand Prairie\"},{\"values\":[329],\"summary\":\"Western Great
        Plains Sandhill Steppe\"},{\"values\":[330],\"summary\":\"Western Great Plains
        Mesquite Woodland and Shrubland\"},{\"values\":[331],\"summary\":\"Western
        Great Plains Shortgrass Prairie\"},{\"values\":[332],\"summary\":\"Arkansas
        Valley Prairie and Woodland\"},{\"values\":[333],\"summary\":\"Central Tallgrass
        Prairie\"},{\"values\":[334],\"summary\":\"North-Central Interior Oak Savanna\"},{\"values\":[335],\"summary\":\"North-Central
        Interior Sand and Gravel Tallgrass Prairie\"},{\"values\":[336],\"summary\":\"North-Central
        Oak Barrens\"},{\"values\":[337],\"summary\":\"Northern Tallgrass Prairie\"},{\"values\":[338],\"summary\":\"Southeastern
        Great Plains Tallgrass Prairie\"},{\"values\":[339],\"summary\":\"Texas Blackland
        Tallgrass Prairie\"},{\"values\":[340],\"summary\":\"Texas-Louisiana Coastal
        Prairie\"},{\"values\":[341],\"summary\":\"Central Appalachian Pine-Oak Rocky
        Woodland\"},{\"values\":[342],\"summary\":\"Southern Appalachian Grass and
        Shrub Bald\"},{\"values\":[343],\"summary\":\"Southern Appalachian Grass and
        Shrub Bald - Herbaceous Modifier\"},{\"values\":[344],\"summary\":\"Southern
        Appalachian Grass and Shrub Bald - Shrub Modifier\"},{\"values\":[345],\"summary\":\"Central
        Appalachian Alkaline Glade and Woodland\"},{\"values\":[346],\"summary\":\"Central
        Interior Highlands Calcareous Glade and Barrens\"},{\"values\":[347],\"summary\":\"Central
        Interior Highlands Dry Acidic Glade and Barrens\"},{\"values\":[348],\"summary\":\"Cumberland
        Sandstone Glade and Barrens\"},{\"values\":[349],\"summary\":\"Great Lakes
        Alvar\"},{\"values\":[350],\"summary\":\"Nashville Basin Limestone Glade\"},{\"values\":[351],\"summary\":\"Southern
        Ridge and Valley / Cumberland Dry Calcareous Forest\"},{\"values\":[352],\"summary\":\"Southern
        Piedmont Glade and Barrens\"},{\"values\":[353],\"summary\":\"East Gulf Coastal
        Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier\"},{\"values\":[354],\"summary\":\"East
        Gulf Coastal Plain Jackson Prairie and Woodland\"},{\"values\":[355],\"summary\":\"Eastern
        Highland Rim Prairie and Barrens - Dry Modifier\"},{\"values\":[356],\"summary\":\"Coahuilan
        Chaparral\"},{\"values\":[357],\"summary\":\"Madrean Oriental Chaparral\"},{\"values\":[358],\"summary\":\"Mogollon
        Chaparral\"},{\"values\":[359],\"summary\":\"Sonora-Mojave Semi-Desert Chaparral\"},{\"values\":[360],\"summary\":\"California
        Montane Woodland and Chaparral\"},{\"values\":[361],\"summary\":\"Great Basin
        Semi-Desert Chaparral\"},{\"values\":[362],\"summary\":\"Florida Dry Prairie\"},{\"values\":[363],\"summary\":\"Florida
        Peninsula Inland Scrub\"},{\"values\":[364],\"summary\":\"West Gulf Coastal
        Plain Catahoula Barrens\"},{\"values\":[365],\"summary\":\"West Gulf Coastal
        Plain Nepheline Syenite Glade\"},{\"values\":[366],\"summary\":\"East Gulf
        Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier\"},{\"values\":[367],\"summary\":\"West
        Gulf Coastal Plain Northern Calcareous Prairie\"},{\"values\":[368],\"summary\":\"West
        Gulf Coastal Plain Southern Calcareous Prairie\"},{\"values\":[369],\"summary\":\"Acadian-Appalachian
        Subalpine Woodland and Heath-Krummholz\"},{\"values\":[370],\"summary\":\"Atlantic
        and Gulf Coastal Plain Interdunal Wetland\"},{\"values\":[371],\"summary\":\"Atlantic
        Coastal Plain Southern Dune and Maritime Grassland\"},{\"values\":[372],\"summary\":\"Central
        and Upper Texas Coast Dune and Coastal Grassland\"},{\"values\":[373],\"summary\":\"East
        Gulf Coastal Plain Dune and Coastal Grassland\"},{\"values\":[374],\"summary\":\"Great
        Lakes Dune\"},{\"values\":[375],\"summary\":\"Northern Atlantic Coastal Plain
        Dune and Swale\"},{\"values\":[376],\"summary\":\"Northern Atlantic Coastal
        Plain Heathland and Grassland\"},{\"values\":[377],\"summary\":\"South Texas
        Dune and Coastal Grassland\"},{\"values\":[378],\"summary\":\"South Texas
        Sand Sheet Grassland\"},{\"values\":[379],\"summary\":\"Southwest Florida
        Dune and Coastal Grassland\"},{\"values\":[380],\"summary\":\"North Pacific
        Coastal Cliff and Bluff\"},{\"values\":[381],\"summary\":\"North Pacific Maritime
        Coastal Sand Dune and Strand\"},{\"values\":[382],\"summary\":\"Northern California
        Coastal Scrub\"},{\"values\":[383],\"summary\":\"Mediterranean California
        Coastal Bluff\"},{\"values\":[384],\"summary\":\"Mediterranean California
        Northern Coastal Dune\"},{\"values\":[385],\"summary\":\"Mediterranean California
        Southern Coastal Dune\"},{\"values\":[386],\"summary\":\"Atlantic Coastal
        Plain Northern Sandy Beach\"},{\"values\":[387],\"summary\":\"Atlantic Coastal
        Plain Sea Island Beach\"},{\"values\":[388],\"summary\":\"Atlantic Coastal
        Plain Southern Beach\"},{\"values\":[389],\"summary\":\"Florida Panhandle
        Beach Vegetation\"},{\"values\":[390],\"summary\":\"Louisiana Beach\"},{\"values\":[391],\"summary\":\"Northern
        Atlantic Coastal Plain Sandy Beach\"},{\"values\":[392],\"summary\":\"Texas
        Coastal Bend Beach\"},{\"values\":[393],\"summary\":\"Upper Texas Coast Beach\"},{\"values\":[394],\"summary\":\"0\"},{\"values\":[395],\"summary\":\"Mediterranean
        California Serpentine Fen\"},{\"values\":[396],\"summary\":\"Mediterranean
        California Subalpine-Montane Fen\"},{\"values\":[397],\"summary\":\"North
        Pacific Bog and Fen\"},{\"values\":[398],\"summary\":\"Rocky Mountain Subalpine-Montane
        Fen\"},{\"values\":[399],\"summary\":\"Atlantic Coastal Plain Peatland Pocosin\"},{\"values\":[400],\"summary\":\"Southern
        and Central Appalachian Bog and Fen\"},{\"values\":[401],\"summary\":\"Atlantic
        Coastal Plain Central Fresh-Oligohaline Tidal Marsh\"},{\"values\":[402],\"summary\":\"Atlantic
        Coastal Plain Embayed Region Tidal Freshwater Marsh\"},{\"values\":[403],\"summary\":\"Atlantic
        Coastal Plain Northern Fresh and Oligohaline Tidal Marsh\"},{\"values\":[404],\"summary\":\"Florida
        Big Bend Fresh-Oligohaline Tidal Marsh\"},{\"values\":[405],\"summary\":\"Atlantic
        Coastal Plain Depression Pondshore\"},{\"values\":[406],\"summary\":\"Atlantic
        Coastal Plain Large Natural Lakeshore\"},{\"values\":[407],\"summary\":\"Central
        Florida Herbaceous Pondshore\"},{\"values\":[408],\"summary\":\"Central Florida
        Herbaceous Seep\"},{\"values\":[409],\"summary\":\"East Gulf Coastal Plain
        Savanna and Wet Prairie\"},{\"values\":[410],\"summary\":\"East Gulf Coastal
        Plain Depression Pondshore\"},{\"values\":[411],\"summary\":\"Floridian Highlands
        Freshwater Marsh\"},{\"values\":[412],\"summary\":\"Southern Coastal Plain
        Herbaceous Seepage Bog\"},{\"values\":[413],\"summary\":\"Southern Coastal
        Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\"},{\"values\":[414],\"summary\":\"Southern
        Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\"},{\"values\":[415],\"summary\":\"Texas-Louisiana
        Coastal Prairie Slough\"},{\"values\":[416],\"summary\":\"Central Interior
        and Appalachian Shrub-Herbaceous Wetland Systems\"},{\"values\":[417],\"summary\":\"Great
        Lakes Coastal Marsh Systems\"},{\"values\":[418],\"summary\":\"0\"},{\"values\":[419],\"summary\":\"0\"},{\"values\":[420],\"summary\":\"Laurentian-Acadian
        Shrub-Herbaceous Wetland Systems\"},{\"values\":[421],\"summary\":\"0\"},{\"values\":[422],\"summary\":\"Eastern
        Great Plains Wet Meadow, Prairie  and Marsh\"},{\"values\":[423],\"summary\":\"Great
        Lakes Wet-Mesic Lakeplain Prairie\"},{\"values\":[424],\"summary\":\"Great
        Plains Prairie Pothole\"},{\"values\":[425],\"summary\":\"Western Great Plains
        Closed Depression Wetland\"},{\"values\":[426],\"summary\":\"Western Great
        Plains Depressional Wetland Systems\"},{\"values\":[427],\"summary\":\"Western
        Great Plains Open Freshwater Depression Wetland\"},{\"values\":[428],\"summary\":\"Cumberland
        Riverscour\"},{\"values\":[429],\"summary\":\"Inter-Mountain Basins Interdunal
        Swale Wetland\"},{\"values\":[430],\"summary\":\"North Pacific Avalanche Chute
        Shrubland\"},{\"values\":[431],\"summary\":\"North Pacific Intertidal Freshwater
        Wetland\"},{\"values\":[432],\"summary\":\"Temperate Pacific Freshwater Emergent
        Marsh\"},{\"values\":[433],\"summary\":\"Temperate Pacific Freshwater Mudflat\"},{\"values\":[434],\"summary\":\"Columbia
        Plateau Vernal Pool\"},{\"values\":[435],\"summary\":\"Northern California
        Claypan Vernal Pool\"},{\"values\":[436],\"summary\":\"Northern Rocky Mountain
        Wooded Vernal Pool\"},{\"values\":[437],\"summary\":\"Columbia Plateau Silver
        Sagebrush Seasonally Flooded Shrub-Steppe\"},{\"values\":[438],\"summary\":\"Rocky
        Mountain Alpine-Montane Wet Meadow\"},{\"values\":[439],\"summary\":\"Rocky
        Mountain Subalpine-Montane Riparian Shrubland\"},{\"values\":[440],\"summary\":\"Temperate
        Pacific Montane Wet Meadow\"},{\"values\":[441],\"summary\":\"Willamette Valley
        Wet Prairie\"},{\"values\":[442],\"summary\":\"Chihuahuan-Sonoran Desert Bottomland
        and Swale Grassland\"},{\"values\":[443],\"summary\":\"North American Arid
        West Emergent Marsh\"},{\"values\":[444],\"summary\":\"North American Warm
        Desert Riparian Mesquite Bosque\"},{\"values\":[445],\"summary\":\"Western
        Great Plains Saline Depression Wetland\"},{\"values\":[446],\"summary\":\"Acadian
        Salt Marsh and Estuary Systems\"},{\"values\":[447],\"summary\":\"Atlantic
        Coastal Plain Central Salt and Brackish Tidal Marsh\"},{\"values\":[448],\"summary\":\"Atlantic
        Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\"},{\"values\":[449],\"summary\":\"Atlantic
        Coastal Plain Indian River Lagoon Tidal Marsh\"},{\"values\":[450],\"summary\":\"Atlantic
        Coastal Plain Northern Tidal Salt Marsh\"},{\"values\":[451],\"summary\":\"Florida
        Big Bend Salt-Brackish Tidal Marsh\"},{\"values\":[452],\"summary\":\"Gulf
        and Atlantic Coastal Plain Tidal Marsh Systems\"},{\"values\":[453],\"summary\":\"Mississippi
        Sound Salt and Brackish Tidal Marsh\"},{\"values\":[454],\"summary\":\"Texas
        Saline Coastal Prairie\"},{\"values\":[455],\"summary\":\"Temperate Pacific
        Tidal Salt and Brackish Marsh\"},{\"values\":[456],\"summary\":\"Inter-Mountain
        Basins Alkaline Closed Depression\"},{\"values\":[457],\"summary\":\"Inter-Mountain
        Basins Greasewood Flat\"},{\"values\":[458],\"summary\":\"Inter-Mountain Basins
        Playa\"},{\"values\":[459],\"summary\":\"North American Warm Desert Playa\"},{\"values\":[460],\"summary\":\"Apacherian-Chihuahuan
        Mesquite Upland Scrub\"},{\"values\":[461],\"summary\":\"Apacherian-Chihuahuan
        Semi-Desert Grassland and Steppe\"},{\"values\":[462],\"summary\":\"Chihuahuan
        Creosotebush, Mixed Desert and Thorn Scrub\"},{\"values\":[463],\"summary\":\"Chihuahuan
        Gypsophilous Grassland and Steppe\"},{\"values\":[464],\"summary\":\"Chihuahuan
        Loamy Plains Desert Grassland\"},{\"values\":[465],\"summary\":\"Chihuahuan
        Mixed Desert and Thorn Scrub\"},{\"values\":[466],\"summary\":\"Chihuahuan
        Sandy Plains Semi-Desert Grassland\"},{\"values\":[467],\"summary\":\"Chihuahuan
        Stabilized Coppice Dune and Sand Flat Scrub\"},{\"values\":[468],\"summary\":\"Chihuahuan
        Succulent Desert Scrub\"},{\"values\":[469],\"summary\":\"Madrean Juniper
        Savanna\"},{\"values\":[470],\"summary\":\"Mojave Mid-Elevation Mixed Desert
        Scrub\"},{\"values\":[471],\"summary\":\"North American Warm Desert Active
        and Stabilized Dune\"},{\"values\":[472],\"summary\":\"Sonora-Mojave Creosotebush-White
        Bursage Desert Scrub\"},{\"values\":[473],\"summary\":\"Sonoran Mid-Elevation
        Desert Scrub\"},{\"values\":[474],\"summary\":\"Sonoran Paloverde-Mixed Cacti
        Desert Scrub\"},{\"values\":[475],\"summary\":\"Chihuahuan Mixed Salt Desert
        Scrub\"},{\"values\":[476],\"summary\":\"Sonora-Mojave Mixed Salt Desert Scrub\"},{\"values\":[477],\"summary\":\"North
        American Warm Desert Wash\"},{\"values\":[478],\"summary\":\"South Texas Lomas\"},{\"values\":[479],\"summary\":\"Tamaulipan
        Calcareous Thornscrub\"},{\"values\":[480],\"summary\":\"Tamaulipan Clay Grassland\"},{\"values\":[481],\"summary\":\"Tamaulipan
        Mesquite Upland Scrub\"},{\"values\":[482],\"summary\":\"Tamaulipan Mixed
        Deciduous Thornscrub\"},{\"values\":[483],\"summary\":\"Tamaulipan Savanna
        Grassland\"},{\"values\":[484],\"summary\":\"Inter-Mountain Basins Mat Saltbush
        Shrubland\"},{\"values\":[485],\"summary\":\"Inter-Mountain Basins Mixed Salt
        Desert Scrub\"},{\"values\":[486],\"summary\":\"Inter-Mountain Basins Wash\"},{\"values\":[487],\"summary\":\"Columbia
        Plateau Steppe and Grassland\"},{\"values\":[488],\"summary\":\"Great Basin
        Xeric Mixed Sagebrush Shrubland\"},{\"values\":[489],\"summary\":\"Inter-Mountain
        Basins Big Sagebrush Shrubland\"},{\"values\":[490],\"summary\":\"Inter-Mountain
        Basins Big Sagebrush Steppe\"},{\"values\":[491],\"summary\":\"Inter-Mountain
        Basins Montane Sagebrush Steppe\"},{\"values\":[492],\"summary\":\"Colorado
        Plateau Mixed Low Sagebrush Shrubland\"},{\"values\":[493],\"summary\":\"Columbia
        Plateau Low Sagebrush Steppe\"},{\"values\":[494],\"summary\":\"Columbia Plateau
        Scabland Shrubland\"},{\"values\":[495],\"summary\":\"Wyoming Basins Dwarf
        Sagebrush Shrubland and Steppe\"},{\"values\":[496],\"summary\":\"Colorado
        Plateau Blackbrush-Mormon-tea Shrubland\"},{\"values\":[497],\"summary\":\"Inter-Mountain
        Basins Semi-Desert Grassland\"},{\"values\":[498],\"summary\":\"Inter-Mountain
        Basins Semi-Desert Shrub Steppe\"},{\"values\":[499],\"summary\":\"Southern
        Colorado Plateau Sand Shrubland\"},{\"values\":[500],\"summary\":\"Acadian-Appalachian
        Alpine Tundra\"},{\"values\":[501],\"summary\":\"Rocky Mountain Alpine Dwarf-Shrubland\"},{\"values\":[502],\"summary\":\"Rocky
        Mountain Alpine Fell-Field\"},{\"values\":[503],\"summary\":\"Rocky Mountain
        Alpine Turf\"},{\"values\":[504],\"summary\":\"Mediterranean California Alpine
        Dry Tundra\"},{\"values\":[505],\"summary\":\"Mediterranean California Alpine
        Fell-Field\"},{\"values\":[506],\"summary\":\"North Pacific Dry and Mesic
        Alpine Dwarf-Shrubland, Fell-field and Meadow\"},{\"values\":[507],\"summary\":\"Rocky
        Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\"},{\"values\":[508],\"summary\":\"Temperate
        Pacific Intertidal Mudflat\"},{\"values\":[509],\"summary\":\"Mediterranean
        California Eelgrass Bed\"},{\"values\":[510],\"summary\":\"North Pacific Maritime
        Eelgrass Bed\"},{\"values\":[511],\"summary\":\"South-Central Interior Large
        Floodplain - Herbaceous Modifier\"},{\"values\":[512],\"summary\":\"East Gulf
        Coastal Plain Large River Floodplain Forest - Herbaceous Modifier\"},{\"values\":[513],\"summary\":\"Temperate
        Pacific Freshwater Aquatic Bed\"},{\"values\":[514],\"summary\":\"Central
        California Coast Ranges Cliff and Canyon\"},{\"values\":[515],\"summary\":\"Mediterranean
        California Serpentine Barrens\"},{\"values\":[516],\"summary\":\"Southern
        California Coast Ranges Cliff and Canyon\"},{\"values\":[517],\"summary\":\"Central
        Interior Acidic Cliff and Talus\"},{\"values\":[518],\"summary\":\"Central
        Interior Calcareous Cliff and Talus\"},{\"values\":[519],\"summary\":\"East
        Gulf Coastal Plain Dry Chalk Bluff\"},{\"values\":[520],\"summary\":\"North-Central
        Appalachian Acidic Cliff and Talus\"},{\"values\":[521],\"summary\":\"North-Central
        Appalachian Circumneutral Cliff and Talus\"},{\"values\":[522],\"summary\":\"Southern
        Appalachian Montane Cliff\"},{\"values\":[523],\"summary\":\"Southern Interior
        Acid Cliff\"},{\"values\":[524],\"summary\":\"Southern Interior Calcareous
        Cliff\"},{\"values\":[525],\"summary\":\"Southern Piedmont Cliff\"},{\"values\":[526],\"summary\":\"Southern
        Appalachian Granitic Dome\"},{\"values\":[527],\"summary\":\"Southern Appalachian
        Rocky Summit\"},{\"values\":[528],\"summary\":\"Southern Piedmont Granite
        Flatrock\"},{\"values\":[529],\"summary\":\"Rocky Mountain Cliff, Canyon and
        Massive Bedrock\"},{\"values\":[530],\"summary\":\"Klamath-Siskiyou Cliff
        and Outcrop\"},{\"values\":[531],\"summary\":\"North Pacific Montane Massive
        Bedrock, Cliff and Talus\"},{\"values\":[532],\"summary\":\"North Pacific
        Serpentine Barren\"},{\"values\":[533],\"summary\":\"North Pacific Active
        Volcanic Rock and Cinder Land\"},{\"values\":[534],\"summary\":\"Sierra Nevada
        Cliff and Canyon\"},{\"values\":[535],\"summary\":\"Western Great Plains Badland\"},{\"values\":[536],\"summary\":\"Southwestern
        Great Plains Canyon\"},{\"values\":[537],\"summary\":\"Western Great Plains
        Cliff and Outcrop\"},{\"values\":[538],\"summary\":\"North American Warm Desert
        Badland\"},{\"values\":[539],\"summary\":\"North American Warm Desert Bedrock
        Cliff and Outcrop\"},{\"values\":[540],\"summary\":\"North American Warm Desert
        Pavement\"},{\"values\":[541],\"summary\":\"North American Warm Desert Volcanic
        Rockland\"},{\"values\":[542],\"summary\":\"Colorado Plateau Mixed Bedrock
        Canyon and Tableland\"},{\"values\":[543],\"summary\":\"Columbia Plateau Ash
        and Tuff Badland\"},{\"values\":[544],\"summary\":\"Geysers and Hot Springs\"},{\"values\":[545],\"summary\":\"Inter-Mountain
        Basins Active and Stabilized Dune\"},{\"values\":[546],\"summary\":\"Inter-Mountain
        Basins Cliff and Canyon\"},{\"values\":[547],\"summary\":\"Inter-Mountain
        Basins Shale Badland\"},{\"values\":[548],\"summary\":\"Inter-Mountain Basins
        Volcanic Rock and Cinder Land\"},{\"values\":[549],\"summary\":\"Rocky Mountain
        Alpine Bedrock and Scree\"},{\"values\":[550],\"summary\":\"Mediterranean
        California Alpine Bedrock and Scree\"},{\"values\":[551],\"summary\":\"North
        Pacific Alpine and Subalpine Bedrock and Scree\"},{\"values\":[552],\"summary\":\"Unconsolidated
        Shore\"},{\"values\":[553],\"summary\":\"Undifferentiated Barren Land\"},{\"values\":[554],\"summary\":\"North
        American Alpine Ice Field\"},{\"values\":[555],\"summary\":\"Orchards Vineyards
        and Other High Structure Agriculture\"},{\"values\":[556],\"summary\":\"Cultivated
        Cropland\"},{\"values\":[557],\"summary\":\"Pasture/Hay\"},{\"values\":[558],\"summary\":\"Introduced
        Upland Vegetation - Annual Grassland\"},{\"values\":[559],\"summary\":\"Introduced
        Upland Vegetation - Perennial Grassland and Forbland\"},{\"values\":[560],\"summary\":\"Modified/Managed
        Southern Tall Grassland\"},{\"values\":[561],\"summary\":\"Introduced Upland
        Vegetation - Shrub\"},{\"values\":[562],\"summary\":\"Introduced Riparian
        and Wetland Vegetation\"},{\"values\":[563],\"summary\":\"Introduced Upland
        Vegetation - Treed\"},{\"values\":[564],\"summary\":\"0\"},{\"values\":[565],\"summary\":\"Disturbed,
        Non-specific\"},{\"values\":[566],\"summary\":\"Recently Logged Areas\"},{\"values\":[567],\"summary\":\"Harvested
        Forest - Grass/Forb Regeneration\"},{\"values\":[568],\"summary\":\"Harvested
        Forest-Shrub Regeneration\"},{\"values\":[569],\"summary\":\"Harvested Forest
        - Northwestern Conifer Regeneration\"},{\"values\":[570],\"summary\":\"Recently
        Burned\"},{\"values\":[571],\"summary\":\"Recently burned grassland\"},{\"values\":[572],\"summary\":\"Recently
        burned shrubland\"},{\"values\":[573],\"summary\":\"Recently burned forest\"},{\"values\":[574],\"summary\":\"Disturbed/Successional
        - Grass/Forb Regeneration\"},{\"values\":[575],\"summary\":\"Disturbed/Successional
        - Shrub Regeneration\"},{\"values\":[576],\"summary\":\"Disturbed/Successional
        - Recently Chained Pinyon-Juniper\"},{\"values\":[577],\"summary\":\"Open
        Water (Aquaculture)\"},{\"values\":[578],\"summary\":\"Open Water (Brackish/Salt)\"},{\"values\":[579],\"summary\":\"Open
        Water (Fresh)\"},{\"values\":[580],\"summary\":\"Quarries, Mines, Gravel Pits
        and Oil Wells\"},{\"values\":[581],\"summary\":\"Developed, Open Space\"},{\"values\":[582],\"summary\":\"Developed,
        Low Intensity\"},{\"values\":[583],\"summary\":\"Developed, Medium Intensity\"},{\"values\":[584],\"summary\":\"Developed,
        High Intensity\"}]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"usgs-gap\",\"stac_extensions\":[\"https://stac-extensions.github.io/label/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"U.S.-wide
        land cover information for 2011\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-17A2HGF-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2HGF-061/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2HGF-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf\",\"title\":\"MOD17
        User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A2HGF\",\"title\":\"MOD17A2HGF
        file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A2HGF\",\"title\":\"MYD17A2HGF
        file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD17A2HGF.061\",\"title\":\"LP
        DAAC - MOD17A2HGF\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD17A2HGF.061\",\"title\":\"LP
        DAAC - MYD17A2HGF\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP
        DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-17A2HGF-061\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS
        Gross Primary Productivity 8-Day Gap-Filled\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A2HGF-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS
        Gross Primary Productivity 8-Day Gap-Filled thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-17A2HGF-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-02-18T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Vegetation\",\"Global\",\"MOD17A2HGF\",\"MYD17A2HGF\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA
        LP DAAC at the USGS EROS Center\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"aqua\",\"terra\"],\"instruments\":[\"modis\"]},\"description\":\"The
        Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day
        composite of values with 500 meter (m) pixel size based on the radiation use
        efficiency concept that can be potentially used as inputs to data models to
        calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry
        of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data
        product includes information about GPP and Net Photosynthesis (PSN). The PSN
        band values are the GPP less the Maintenance Respiration (MR). The data product
        also contains a PSN Quality Control (QC) layer. The quality layer contains
        quality information for both the GPP and the PSN. This product will be generated
        at the end of each year when the entire yearly 8-day 15A2H is available. Hence,
        the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality
        inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active
        Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel.
        If any LAI/FPAR pixel did not meet the quality screening criteria, its value
        is determined through linear interpolation. However, users cannot get this
        product in near-real time because it will be generated only at the end of
        a given year.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source
        data containing all bands\"},\"Gpp_500m\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Gross Primary Productivity\",\"raster:bands\":[{\"unit\":\"kg
        C/m^2\",\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal
        Geographic Data Committee (FGDC) Metadata\"},\"PsnNet_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Net
        Photosynthesis\",\"raster:bands\":[{\"unit\":\"kg C/m^2\",\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"Psn_QC_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Quality
        control indicators\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":500}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"modis\",\"msft:container\":\"modis-061\",\"stac_extensions\":[\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.5067/MODIS/MOD17A2HGF.061\",\"citation\":\"Running,
        S., &amp; Zhao, M. (2021). <i>MODIS/Terra Gross Primary Productivity Gap-Filled
        8-Day L4 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes
        DAAC. https://doi.org/10.5067/MODIS/MOD17A2HGF.061\"},{\"doi\":\"10.5067/MODIS/MYD17A2HGF.061\",\"citation\":\"Running,
        S., &amp; Zhao, M. (2021). <i>MODIS/Aqua Gross Primary Productivity Gap-Filled
        8-Day L4 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes
        DAAC. https://doi.org/10.5067/MODIS/MYD17A2HGF.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS
        Gross Primary Productivity 8-Day Gap-Filled\",\"msft:region\":\"westeurope\"},{\"id\":\"planet-nicfi-visual\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual\"},{\"rel\":\"license\",\"href\":\"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf\",\"type\":\"application/pdf\",\"title\":\"Participant
        License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf\",\"type\":\"application/pdf\",\"title\":\"Participant
        License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://www.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI
        Program - Satellite Imagery and Monitoring | Planet\"},{\"rel\":\"documentation\",\"href\":\"https://developers.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI
        Program Resource Center\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-visual\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Planet-NICFI
        Basemaps (Visual)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-visual-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/planet-nicfi-visual.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-34.161818157002,180.0,30.145127179625]]},\"temporal\":{\"interval\":[[\"2015-12-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Planet\",\"NICFI\",\"Satellite\",\"Tropics\",\"Imagery\"],\"providers\":[{\"url\":\"http://planet.com\",\"name\":\"Planet\",\"roles\":[\"processor\",\"producer\",\"licensor\"],\"description\":\"Contact
        Planet at [planet.com/contact-sales](https://www.planet.com/contact-sales/)\"},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[4.77],\"eo:bands\":[{\"name\":\"Red\",\"common_name\":\"red\",\"description\":\"visible
        red\"},{\"name\":\"Green\",\"common_name\":\"green\",\"description\":\"visible
        green\"},{\"name\":\"Blue\",\"common_name\":\"blue\",\"description\":\"visible
        blue\"}],\"planet-nicfi:cadence\":[\"biannual\",\"monthly\"],\"planet-nicfi:percent_covered\":{\"maximum\":100,\"minimum\":0}},\"description\":\"*Note:
        Assets in this collection are only available to winners of the [GEO-Microsoft
        Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528).
        Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/)
        and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\n\\nThrough
        Norway\u2019s International Climate & Forests Initiative (NICFI), users can
        access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s
        tropics in order to help reduce and reverse the loss of tropical forests,
        combat climate change, conserve biodiversity, and facilitate sustainable development.\\n\\nIn
        support of NICFI\u2019s mission, you can use this data for a number of projects
        including, but not limited to:\\n\\n* Advance scientific research about the
        world\u2019s tropical forests and the critical services they provide.\\n*
        Implement and improve policies for sustainable forest management and land
        use in developing tropical forest countries and jurisdictions.\\n* Increase
        transparency and accountability in the tropics.\\n* Protect and improve the
        rights of indigenous peoples and local communities in tropical forest countries.\\n*
        Innovate solutions towards reducing pressure on forests from global commodities
        and financial markets.\\n* In short, the primary purpose of the NICFI Program
        is to support reducing and reversing the loss of tropical forests, contributing
        to combating climate change, conserving biodiversity, contributing to forest
        regrowth, restoration, and enhancement, and facilitating sustainable development,
        all of which must be Non-Commercial Use.\\n\\nTo learn how more about the
        NICFI program, streaming and downloading basemaps please read the [NICFI Data
        Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\\n\\nThis
        collection contains both monthly and biannual mosaics. Biannual mosaics are
        available from December 2015 - August 2020. Monthly mosaics are available
        from September 2020. The STAC items include a `planet-nicfi:cadence` field
        indicating the type of mosaic.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Data\",\"description\":\"a
        'true-colour' representation of spatially accurate data with minimized haze,
        illumination, and topographic effects\"},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"planet-nicfi\",\"msft:container\":\"nicfi\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"planet\",\"msft:short_description\":\"Planet's
        high-resolution, analysis-ready mosaics of the world's tropics\",\"msft:region\":\"westeurope\"},{\"id\":\"gbif\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif\"},{\"rel\":\"license\",\"href\":\"https://www.gbif.org/terms\",\"type\":\"text/html\",\"title\":\"GBIF
        Terms of Use\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gbif\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Global
        Biodiversity Information Facility (GBIF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gbif.png\",\"type\":\"image/png\",\"title\":\"Forest
        Inventory and Analysis\"},\"geoparquet-items\":{\"href\":\"abfs://items/gbif.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2021-04-13T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"GBIF\",\"Biodiversity\",\"Species\"],\"providers\":[{\"url\":\"https://www.gbif.org/\",\"name\":\"Global
        Biodiversity Information Facility\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"The
        [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is
        an international network and data infrastructure funded by the world's governments,
        providing global data that document the occurrence of species. GBIF currently
        integrates datasets documenting over 1.6 billion species occurrences.\\n\\nThe
        GBIF occurrence dataset combines data from a wide array of sources, including
        specimen-related data from natural history museums, observations from citizen
        science networks, and automated environmental surveys. While these data are
        constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots
        are taken and made available here. \\n\\nData are stored in [Parquet](https://parquet.apache.org/)
        format; the Parquet file schema is described below.  Most field names correspond
        to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and
        have been interpreted by GBIF's systems to align taxonomy, location, dates,
        etc.  Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\\n\\nPlease
        refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines)
        for information about how to cite GBIF data in publications.. For analyses
        using the whole dataset, please use the following citation:\\n\\n> GBIF.org
        ([Date]) GBIF Occurrence Data [DOI of dataset]\\n\\nFor analyses where data
        are significantly filtered, please track the datasetKeys used and use a \\\"[derived
        dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\\\" record
        for citing the data.\\n\\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/)
        contains a number of articles that can help you analyze GBIF data.\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset
        root\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"gbifid\",\"type\":\"int64\",\"description\":\"GBIF's
        identifier for the occurrence\"},{\"name\":\"datasetkey\",\"type\":\"byte_array\",\"description\":\"GBIF's
        UUID for the [dataset](https://www.gbif.org/developer/registry#datasets) containing
        this occurrence\"},{\"name\":\"occurrenceid\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:occurrenceID](https://dwc.tdwg.org/terms/#occurrenceID).\"},{\"name\":\"kingdom\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:kingdom](https://dwc.tdwg.org/terms/#kingdom).  This field has been aligned
        with the [GBIF backbone taxonomy](https://doi.org/10.15468/39omei).\"},{\"name\":\"phylum\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:phylum](https://dwc.tdwg.org/terms/#phylum).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"class\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:class](https://dwc.tdwg.org/terms/#class).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"order\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:order](https://dwc.tdwg.org/terms/#order).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"family\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:family](https://dwc.tdwg.org/terms/#family).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"genus\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:genus](https://dwc.tdwg.org/terms/#genus).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"species\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:species](https://dwc.tdwg.org/terms/#species).  This field has been aligned
        with the GBIF backbone taxonomy.\"},{\"name\":\"infraspecificepithet\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:infraspecificEpithet](https://dwc.tdwg.org/terms/#infraspecificEpithet).
        \ This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"taxonrank\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:taxonRank](https://dwc.tdwg.org/terms/#taxonRank).  This field has been
        aligned with the GBIF backbone taxonomy.\"},{\"name\":\"scientificname\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:scientificName](https://dwc.tdwg.org/terms/#scientificName).  This field
        has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"verbatimscientificname\",\"type\":\"byte_array\",\"description\":\"The
        scientific name as provided by the data publisher\"},{\"name\":\"verbatimscientificnameauthorship\",\"type\":\"byte_array\",\"description\":\"The
        scientific name authorship provided by the data publisher.\"},{\"name\":\"countrycode\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:countryCode](https://dwc.tdwg.org/terms/#countryCode).  GBIF's interpretation
        has set this to an ISO 3166-2 code.\"},{\"name\":\"locality\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:locality](https://dwc.tdwg.org/terms/#locality).\"},{\"name\":\"stateprovince\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:stateProvince](https://dwc.tdwg.org/terms/#stateProvince).\"},{\"name\":\"occurrencestatus\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:occurrenceStatus](https://dwc.tdwg.org/terms/#occurrenceStatus). Either
        the value `PRESENT` or `ABSENT`.  **Many users will wish to filter for `PRESENT`
        data.**\"},{\"name\":\"individualcount\",\"type\":\"int32\",\"description\":\"See
        [dwc:individualCount](https://dwc.tdwg.org/terms/#individualCount).\"},{\"name\":\"publishingorgkey\",\"type\":\"byte_array\",\"description\":\"GBIF's
        UUID for the [organization](https://www.gbif.org/developer/registry#organizations)
        publishing this occurrence.\"},{\"name\":\"decimallatitude\",\"type\":\"double\",\"description\":\"See
        [dwc:decimalLatitude](https://dwc.tdwg.org/terms/#decimalLatitude).  GBIF's
        interpretation has normalized this to a WGS84 coordinate.\"},{\"name\":\"decimallongitude\",\"type\":\"double\",\"description\":\"See
        [dwc:decimalLongitude](https://dwc.tdwg.org/terms/#decimalLongitude).  GBIF's
        interpretation has normalized this to a WGS84 coordinate.\"},{\"name\":\"coordinateuncertaintyinmeters\",\"type\":\"double\",\"description\":\"See
        [dwc:coordinateUncertaintyInMeters](https://dwc.tdwg.org/terms/#coordinateUncertaintyInMeters).\"},{\"name\":\"coordinateprecision\",\"type\":\"double\",\"description\":\"See
        [dwc:coordinatePrecision](https://dwc.tdwg.org/terms/#coordinatePrecision).\"},{\"name\":\"elevation\",\"type\":\"double\",\"description\":\"See
        [dwc:elevation](https://dwc.tdwg.org/terms/#elevation).  If provided by the
        data publisher, GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"elevationaccuracy\",\"type\":\"double\",\"description\":\"See
        [dwc:elevationAccuracy](https://dwc.tdwg.org/terms/#elevationAccuracy).  If
        provided by the data publisher, GBIF's interpretation has normalized this
        value to metres.\"},{\"name\":\"depth\",\"type\":\"double\",\"description\":\"See
        [dwc:depth](https://dwc.tdwg.org/terms/#depth).  If provided by the data publisher,
        GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"depthaccuracy\",\"type\":\"double\",\"description\":\"See
        [dwc:depthAccuracy](https://dwc.tdwg.org/terms/#depthAccuracy).  If provided
        by the data publisher, GBIF's interpretation has normalized this value to
        metres.\"},{\"name\":\"eventdate\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:eventDate](https://dwc.tdwg.org/terms/#eventDate).  GBIF's interpretation
        has normalized this value to an ISO 8601 date with a local time.\"},{\"name\":\"day\",\"type\":\"int32\",\"description\":\"See
        [dwc:day](https://dwc.tdwg.org/terms/#day).\"},{\"name\":\"month\",\"type\":\"int32\",\"description\":\"See
        [dwc:month](https://dwc.tdwg.org/terms/#month).\"},{\"name\":\"year\",\"type\":\"int32\",\"description\":\"See
        [dwc:year](https://dwc.tdwg.org/terms/#year).\"},{\"name\":\"taxonkey\",\"type\":\"int32\",\"description\":\"The
        numeric identifier for the [taxon](https://www.gbif.org/developer/species#nameUsages)
        in GBIF's backbone taxonomy corresponding to `scientificname`.\"},{\"name\":\"specieskey\",\"type\":\"int32\",\"description\":\"The
        numeric identifier for the taxon in GBIF's backbone taxonomy corresponding
        to `species`.\"},{\"name\":\"basisofrecord\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:basisOfRecord](https://dwc.tdwg.org/terms/#basisOfRecord).  One of `PRESERVED_SPECIMEN`,
        `FOSSIL_SPECIMEN`, `LIVING_SPECIMEN`, `OBSERVATION`, `HUMAN_OBSERVATION`,
        `MACHINE_OBSERVATION`, `MATERIAL_SAMPLE`, `LITERATURE`, `UNKNOWN`.\"},{\"name\":\"institutioncode\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:institutionCode](https://dwc.tdwg.org/terms/#institutionCode).\"},{\"name\":\"collectioncode\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:collectionCode](https://dwc.tdwg.org/terms/#collectionCode).\"},{\"name\":\"catalognumber\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:catalogNumber](https://dwc.tdwg.org/terms/#catalogNumber).\"},{\"name\":\"recordnumber\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:recordNumber](https://dwc.tdwg.org/terms/#recordNumber).\"},{\"name\":\"identifiedby\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:identifiedBy](https://dwc.tdwg.org/terms/#identifiedBy).\"},{\"name\":\"dateidentified\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:dateIdentified](https://dwc.tdwg.org/terms/#dateIdentified). An ISO 8601
        date.\"},{\"name\":\"license\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:license](https://dwc.tdwg.org/terms/#license). Either [`CC0_1_0`](https://creativecommons.org/publicdomain/zero/1.0/)
        or [`CC_BY_4_0`](https://creativecommons.org/licenses/by/4.0/).  `CC_BY_NC_4_0`
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        [dwc:rightsHolder](https://dwc.tdwg.org/terms/#rightsHolder).\"},{\"name\":\"recordedby\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:recordedBy](https://dwc.tdwg.org/terms/#recordedBy).\"},{\"name\":\"typestatus\",\"type\":\"byte_array\",\"description\":\"See
        [dwc:typeStatus](https://dwc.tdwg.org/terms/#typeStatus).\"},{\"name\":\"establishmentmeans\",\"type\":\"byte_array\",\"description\":\"See
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        for several reasons, including changes to the backbone taxonomy, so this date
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        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,85.0,180.0,-56.0]]},\"temporal\":{\"interval\":[[\"2015-01-01T00:00:00Z\",\"2021-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"version\":\"2.0.0\",\"keywords\":[\"ALOS\",\"JAXA\",\"Remote
        Sensing\",\"Global\"],\"providers\":[{\"url\":\"https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm\",\"name\":\"Japan
        Aerospace Exploration Agency\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://developmentseed.org\",\"name\":\"Development
        Seed\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft
        Planetary Computer\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"ALOS\",\"ALOS-2\"],\"instruments\":[\"PALSAR\",\"PALSAR-2\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"sar:polarizations\":[[\"HH\",\"HV\"],[\"HH\",\"HV\",\"VH\",\"VV\"]],\"sar:instrument_mode\":[\"F\",\"U\"],\"sar:observation_direction\":[\"left\",\"right\"],\"palsar:number_of_polarizations\":[\"D\",\"Q\"]},\"description\":\"Global
        25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)\",\"item_assets\":{\"HH\":{\"role\":\"data\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"HH\",\"description\":\"HH
        polarization backscattering coefficient, 16-bit DN.\"},\"HV\":{\"role\":\"data\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"HV\",\"description\":\"HV
        polarization backscattering coefficient, 16-bit DN.\"},\"VH\":{\"role\":\"data\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"VH\",\"description\":\"VH
        polarization backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode
        only).\"},\"VV\":{\"role\":\"data\",\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"title\":\"VV\",\"description\":\"VV polarization
        backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode only).\"},\"date\":{\"role\":\"date\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"date\",\"description\":\"Observation
        date (days since Jan 1, 1970).\"},\"mask\":{\"role\":\"data-mask\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"mask\",\"description\":\"Quality
        Mask\",\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\"}],\"classification:classes\":[{\"name\":\"no_data\",\"value\":0,\"description\":\"No
        data\"},{\"name\":\"water\",\"value\":50,\"description\":\"Water\"},{\"name\":\"lay_over\",\"value\":100,\"description\":\"Lay
        over\"},{\"name\":\"shadowing\",\"value\":150,\"description\":\"Shadowing\"},{\"name\":\"land\",\"value\":255,\"description\":\"Land\"}]},\"linci\":{\"role\":\"local-incidence-angle\",\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"title\":\"linci\",\"description\":\"Local
        incidence angle (degrees).\"},\"metadata\":{\"type\":\"application/xml\",\"title\":\"metadata\",\"description\":\"Product
        metadata file\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"palsar\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\",\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/version/v1.0.0/schema.json\",\"https://stac-extensions.github.io/classification/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"https://pceo.blob.core.windows.net/\",\"msft:short_description\":\"Global
        25m resolution SAR image mosaic.\",\"msft:region\":\"westeurope\"},{\"id\":\"deltares-water-availability\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-water-availability/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-water-availability\"},{\"rel\":\"documentation\",\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf\",\"type\":\"application/pdf\",\"title\":\"User
        Guide\"},{\"rel\":\"license\",\"href\":\"https://cdla.dev/permissive-1-0/\",\"type\":\"text/html\",\"title\":\"Community
        Data License Agreement - Permissive, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/deltares-water-availability\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Deltares
        Global Water Availability\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/deltares-reservoir.jpg\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,90.0,180.0,-90.0]]},\"temporal\":{\"interval\":[[\"1970-01-01T00:00:00Z\",\"2020-12-31T00:00:00Z\"]]}},\"license\":\"CDLA-Permissive-1.0\",\"keywords\":[\"Deltares\",\"Water
        availability\",\"Reservoir\",\"Water\",\"Precipitation\"],\"providers\":[{\"url\":\"https://www.deltares.nl/en/\",\"name\":\"Deltares\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"deltares:reservoir\":[\"ERA5\",\"CHIRPS\",\"EOBS\",\"NLDAS\",\"BOM\"]},\"description\":\"[Deltares](https://www.deltares.nl/en/)
        has produced a hydrological model approach to simulate historical daily reservoir
        variations for 3,236 locations across the globe for the period 1970-2020 using
        the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/)
        model. The model outputs long-term daily information on reservoir volume,
        inflow and outflow dynamics, as well as information on upstream hydrological
        forcing.\\n\\nThey hydrological model was forced with 5 different precipitation
        products. Two products (ERA5 and CHIRPS) are available at the global scale,
        while for Europe, USA and Australia a regional product was use (i.e. EOBS,
        NLDAS and BOM, respectively). Using these different precipitation products,
        it becomes possible to assess the impact of uncertainty in the model forcing.
        A different number of basins upstream of reservoirs are simulated, given the
        spatial coverage of each precipitation product.\\n\\nSee the complete [methodology
        documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf)
        for more information.\\n\\n## Dataset coverages\\n\\n| Name   | Scale                    |
        Period    | Number of basins |\\n|--------|--------------------------|-----------|------------------|\\n|
        ERA5   | Global                   | 1967-2020 | 3236             |\\n| CHIRPS
        | Global (+/- 50 latitude) | 1981-2020 | 2951             |\\n| EOBS   | Europe/North
        Africa      | 1979-2020 | 682              |\\n| NLDAS  | USA                      |
        1979-2020 | 1090             |\\n| BOM    | Australia                | 1979-2020
        | 116              |\\n\\n## STAC Metadata\\n\\nThis STAC collection includes
        one STAC item per dataset. The item includes a `deltares:reservoir` property
        that can be used to query for the URL of a specific dataset.\\n\\n## Contact\\n\\nFor
        questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).\",\"item_assets\":{\"data\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Flood
        Map\",\"description\":\"Inundation maps of flood depth using a model that
        takes into account water level attenuation and is forced by sea level.\"},\"index\":{\"type\":\"application/json\",\"roles\":[\"index\"],\"title\":\"Index
        file\",\"description\":\"Kerchunk index file.\"}},\"stac_version\":\"1.0.0\",\"cube:variables\":{\"P\":{\"type\":\"data\",\"unit\":\"mm
        per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average precipitation
        upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        precipitation upstream of reservoir\"},\"ETa\":{\"type\":\"data\",\"unit\":\"mm
        per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average simulated
        actual evapotransporation upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        simulated actual evapotransporation upstream of reservoir\"},\"PET\":{\"type\":\"data\",\"unit\":\"mm
        per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average potential
        evapotranspiration upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        potential evapotranspiration upstream of reservoir\"},\"Melt\":{\"type\":\"data\",\"unit\":\"mm
        per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average simulated
        snow melt upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        simulated snow melt upstream of reservoir\"},\"Snow\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"description\":\"Average
        simulated snow depth upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        simulated snow depth upstream of reservoir\"},\"Temp\":{\"type\":\"data\",\"unit\":\"degrees
        C\",\"attrs\":{\"units\":\"degrees C\",\"description\":\"Average surface temperature
        upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average
        surface temperature upstream of reservoir\"},\"P_res\":{\"type\":\"data\",\"unit\":\"mm
        per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Precipitation
        reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Precipitation
        reservoir\"},\"S_res\":{\"type\":\"data\",\"unit\":\"m3\",\"attrs\":{\"units\":\"m3\",\"description\":\"Simulated
        reservoir volume\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated
        reservoir volume\"},\"Ea_res\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm
        per day\",\"description\":\"Simulated actual evaporation reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated
        actual evaporation reservoir\"},\"Qin_res\":{\"type\":\"data\",\"unit\":\"m3
        per s\",\"attrs\":{\"units\":\"m3 per s\",\"description\":\"Simulated reservoir
        inflow (surface+subsurface)\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated
        reservoir inflow (surface+subsurface)\"},\"FracFull\":{\"type\":\"data\",\"unit\":\"m3\",\"attrs\":{\"units\":\"m3\",\"description\":\"Simulated
        reservoir volume\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated
        reservoir volume\"},\"Qout_res\":{\"type\":\"data\",\"unit\":\"m3 per s\",\"attrs\":{\"units\":\"m3
        per s\",\"description\":\"Simulated reservoir outflow\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated
        reservoir outflow\"},\"latitude\":{\"type\":\"data\",\"unit\":\"degrees\",\"attrs\":{\"units\":\"degrees\",\"description\":\"Latitude
        of reservoir\"},\"dimensions\":[\"GrandID\"],\"description\":\"Latitude of
        reservoir\"},\"longitude\":{\"type\":\"data\",\"unit\":\"degrees\",\"attrs\":{\"units\":\"degrees\",\"description\":\"Longitude
        of reservoir\"},\"dimensions\":[\"GrandID\"],\"description\":\"Longitude of
        reservoir\"}},\"msft:container\":\"reservoirs\",\"cube:dimensions\":{\"time\":{\"step\":\"P1DT0H0M0S\",\"type\":\"temporal\",\"extent\":[null,\"2020-12-31T00:00:00Z\"]},\"GrandID\":{\"type\":\"identifier\",\"extent\":[null,null],\"description\":\"GrandID
        number of the reservoir of interest\"},\"ksathorfrac\":{\"type\":\"level\",\"values\":[5,20,50,100,250],\"description\":\"Five
        different value lateral anisotropy values used\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"deltaresreservoirssa\",\"msft:short_description\":\"Historical
        daily reservoir variations.\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-16A3GF-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-16A3GF-061/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-16A3GF-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/931/MOD16_User_Guide_V61.pdf\",\"title\":\"MOD16
        User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD16A3GF\",\"title\":\"MOD16A3GF
        file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD16A3GF\",\"title\":\"MYD16A3GF
        file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD16A3GF.061\",\"title\":\"LP
        DAAC - MOD16A3GF\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD16A3GF.061\",\"title\":\"LP
        DAAC - MYD16A3GF\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP
        DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-16A3GF-061\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS
        Net Evapotranspiration Yearly Gap-Filled\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-16A3GF-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS
        Net Evapotranspiration Yearly Gap-Filled thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-16A3GF-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2001-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Global\",\"Vegetation\",\"MOD16A3GF\",\"MYD16A3GF\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA
        LP DAAC at the USGS EROS Center\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"aqua\",\"terra\"],\"instruments\":[\"modis\"]},\"description\":\"The
        Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1
        Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled
        yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm
        used for the MOD16 data product collection is based on the logic of the Penman-Monteith
        equation, which includes inputs of daily meteorological reanalysis data along
        with MODIS remotely sensed data products such as vegetation property dynamics,
        albedo, and land cover. The product will be generated at the end of each year
        when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled
        product is the improved 16, which has cleaned the poor-quality inputs from
        yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation
        (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any
        LAI/FPAR pixel did not meet the quality screening criteria, its value is determined
        through linear interpolation. However, users cannot get this product in near-real
        time because it will be generated only at the end of a given year. Provided
        in the product are layers for composited ET, LE, Potential ET (PET), and Potential
        LE (PLE) along with a quality control layer. Two low resolution browse images,
        ET and LE, are also available for each granule. The pixel values for the two
        Evapotranspiration layers (ET and PET) are the sum for all days within the
        defined year, and the pixel values for the two Latent Heat layers (LE and
        PLE) are the average of all days within the defined year.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source
        data containing all bands\"},\"ET_500m\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Total of Evapotranspiration\",\"raster:bands\":[{\"unit\":\"kg/m^2/year\",\"scale\":0.1,\"data_type\":\"uint16\",\"spatial_resolution\":500}]},\"LE_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average
        of Latent Heat Flux\",\"raster:bands\":[{\"unit\":\"J/m^2/day\",\"scale\":10000,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"PET_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Total
        Potential Evapotranspiration\",\"raster:bands\":[{\"unit\":\"kg/m^2/year\",\"scale\":0.1,\"data_type\":\"uint16\",\"spatial_resolution\":500}]},\"PLE_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average
        of Potential Latent Heat Flux\",\"raster:bands\":[{\"unit\":\"J/m^2/day\",\"scale\":10000,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal
        Geographic Data Committee (FGDC) Metadata\"},\"ET_QC_500m\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Evapotranspiration
        Quality Assessment\",\"raster:bands\":[{\"unit\":\"Percent\",\"data_type\":\"uint8\",\"spatial_resolution\":500}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"modis\",\"msft:container\":\"modis-061\",\"stac_extensions\":[\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.5067/MODIS/MOD16A3GF.061\",\"citation\":\"Running,
        S., Mu, Q., Zhao, M., &amp; Moreno, A. (2021). <i>MODIS/Terra Net Evapotranspiration
        Gap-Filled Yearly L4 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS
        Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD16A3GF.061\"},{\"doi\":\"10.5067/MODIS/MYD16A3GF.061\",\"citation\":\"Running,
        S., Mu, Q., Zhao, M., &amp; Moreno, A. (2021). <i>MODIS/Aqua Net Evapotranspiration
        Gap-Filled Yearly L4 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS
        Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD16A3GF.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS
        Net Evapotranspiration Yearly Gap-Filled\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-21A2-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-21A2-061/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-21A2-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/620/MOD21_User_Guide_V61.pdf\",\"title\":\"MOD21
        User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21A2\",\"title\":\"MOD21A2
        file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21A2\",\"title\":\"MYD21A2
        file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD21A2.061\",\"title\":\"LP
        DAAC - MOD21A2\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD21A2.061\",\"title\":\"LP
        DAAC - MYD21A2\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP
        DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-21A2-061\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS
        Land Surface Temperature/3-Band Emissivity 8-Day\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-21A2-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS
        Land Surface Temperature/3-Band Emissivity 8-Day thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-21A2-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-02-16T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Global\",\"Temperature\",\"MOD21A2\",\"MYD21A2\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA
        LP DAAC at the USGS EROS Center\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"aqua\",\"terra\"],\"instruments\":[\"modis\"]},\"description\":\"A
        suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface
        Temperature and Emissivity (LST&E) products are available in Collection 6.1.
        The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm
        of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER
        Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses
        the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm
        to dynamically retrieve both the LST and spectral emissivity simultaneously
        from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is
        combined with an improved Water Vapor Scaling (WVS) atmospheric correction
        scheme to stabilize the retrieval during very warm and humid conditions. This
        dataset is an 8-day composite LST product at 1,000 meter spatial resolution
        that uses an algorithm based on a simple averaging method. The algorithm calculates
        the average from all the cloud free 21A1D and 21A1N daily acquisitions from
        the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime
        acquisitions are separate products, the 21A2 contains both daytime and nighttime
        acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical
        Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle,
        and viewing time have separate day and night SDS layers, while the values
        for the MODIS emissivity bands 29, 31, and 32 are the average of both the
        nighttime and daytime acquisitions. Additional details regarding the method
        used to create this Level 3 (L3) product are available in the Algorithm Theoretical
        Basis Document (ATBD).\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source
        data containing all bands\"},\"QC_Day\":{\"type\":\"image/tiff; application=geotiff;
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        G., &amp; Hook, S. (2021). <i>MODIS/Terra Land Surface Temperature/3-Band
        Emissivity 8-Day L3 Global 1km SIN Grid V061</i> [Data set]. NASA EOSDIS Land
        Processes DAAC. https://doi.org/10.5067/MODIS/MOD21A2.061\"},{\"doi\":\"10.5067/MODIS/MYD21A2.061\",\"citation\":\"Hulley,
        G. (2021). <i>MODIS/Aqua Land Surface Temperature/3-Band Emissivity 8-Day
        L3 Global 1km SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC.
        https://doi.org/10.5067/MODIS/MYD21A2.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS
        Land Surface Temperature/3-Band Emissivity 8-Day\",\"msft:region\":\"westeurope\"},{\"id\":\"us-census\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census\"},{\"rel\":\"license\",\"href\":\"https://www.census.gov/about/policies/open-gov/open-data.html\",\"type\":\"text/html\",\"title\":\"U.S.
        Census Open Data Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/us-census\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"US
        Census\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/us-census.png\",\"type\":\"image/png\",\"title\":\"US
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        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.763068,24.523096,-66.949895,49.384358],[-179.148909,51.214183,-129.974167,71.365162],[172.461667,51.357688,179.77847,53.01075],[-178.334698,18.910361,-154.806773,28.402123],[144.618068,13.234189,144.956712,13.654383],[-67.945404,17.88328,-65.220703,18.515683],[144.886331,14.110472,146.064818,20.553802],[-65.085452,17.673976,-64.564907,18.412655],[-171.089874,-14.548699,-168.1433,-11.046934],[-178.334698,18.910361,-154.806773,28.402123]]},\"temporal\":{\"interval\":[[\"2021-08-01T00:00:00Z\",\"2021-08-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"US
        Census Bureau\",\"Administrative boundaries\",\"Population\",\"Demographics\"],\"providers\":[{\"url\":\"https://www.census.gov/en.html\",\"name\":\"United
        States Census Bureau\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://makepath.com/\",\"name\":\"makepath\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"The
        [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html)
        counted every person living in the United States and the five U.S. territories.
        It marked the 24th census in U.S. history and the first time that households
        were invited to respond to the census online.\\n\\nThe tables included on
        the Planetary Computer provide information on population and geographic boundaries
        at various levels of cartographic aggregation.\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset
        root\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"American
        Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)\",\"description\":\"This
        file contains data for legal and statistical [American Indian/Alaska Native
        Areas/Hawaiian Home Lands (AIANNH)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1)
        entities published by the US Census Bureau.\",\"msft:item_name\":\"cb_2020_us_aiannh_500k\"},{\"name\":\"American
        Indian Tribal Subdivisions (AITSN)\",\"description\":\"This file contains
        data on [American Indian Tribal Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1).
        These areas are the legally defined subdivisions of American Indian Reservations
        (AIR), Oklahoma Tribal Statistical Areas (OTSA), and Off-Reservation Trust
        Land (ORTL).\",\"msft:item_name\":\"cb_2020_us_aitsn_500k\"},{\"name\":\"Alaska
        Native Regional Corporations (ANRC)\",\"description\":\"This file contains
        data on [Alaska Native Regional Corporations](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1),
        which are corporations created according to the Alaska Native Claims Settlement
        Act. \",\"msft:item_name\":\"cb_2020_02_anrc_500k\"},{\"name\":\"Tribal Block
        Groups (TBG)\",\"description\":\"This file includes data on [Tribal Block
        Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_26),
        which are subdivisions of Tribal Census Tracts. These block groups can extend
        over multiple AIRs and ORTLs due to areas not meeting Block Group minimum
        population thresholds.\",\"msft:item_name\":\"cb_2020_us_tbg_500k\"},{\"name\":\"Tribal
        Census Tracts (TTRACT)\",\"description\":\"This file includes data on [Tribal
        Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_27)
        which are relatively small statistical subdivisions of AIRs and ORTLs defined
        by federally recognized tribal government officials in partnership with the
        Census Bureau. Due to population thresholds, the Tracts may consist of multiple
        non-contiguous areas.\",\"msft:item_name\":\"cb_2020_us_ttract_500k\"},{\"name\":\"Census
        Block Groups (BG)\",\"description\":\"This file contains data on [Census Block
        Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4).
        These groups are the second smallest geographic grouping. They consist of
        clusters of blocks within the same census tract that share the same first
        digit of their 4-character census block number. Census Block Groups generally
        contain between 600 and 3,000 people and generally cover contiguous areas.\",\"msft:item_name\":\"cb_2020_us_bg_500k\"},{\"name\":\"Census
        Tracts (TRACT)\",\"description\":\"This file contains data on [Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13)
        which are small and relatively permanent statistical subdivisions of a county
        or equivalent entity. Tract population size is generally between 1,200 and
        8,000 people with an ideal size of 4,000. Boundaries tend to follow visible
        and identifiable features and are usually contiguous areas.\",\"msft:item_name\":\"cb_2020_us_tract_500k\"},{\"name\":\"Congressional
        Districts: 116th Congress (CD116)\",\"description\":\"This file contains data
        on the [Congressional Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_9)
        for the 116th Congress. \",\"msft:item_name\":\"cb_2020_us_cd116_500k\"},{\"name\":\"Consolidated
        Cities (CONCITY)\",\"description\":\"This file contains data on [Consolidated
        Cities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_8).
        These are areas where one or several other incorporated places in a county
        or Minor Civil Division are included in a consolidated government but still
        exist as separate legal entities.\",\"msft:item_name\":\"cb_2020_us_concity_500k\"},{\"name\":\"Counties
        (COUNTY)\",\"description\":\"This file contains data on [Counties and Equivalent
        Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_12).
        These are the primary legal divisions of states. Most states use the term
        \\\"counties,\\\" but other terms such as \\\"Parishes,\\\" \\\"Municipios,\\\"
        or \\\"Independent Cities\\\" may be used. \",\"msft:item_name\":\"cb_2020_us_county_500k\"},{\"name\":\"Counties
        within Congressional Districts: 116th Congress (COUNTY_within_CD116)\",\"description\":\"This
        file contains data on Counties within Congressional Districts.\",\"msft:item_name\":\"cb_2020_us_county_within_cd116_500k\"},{\"name\":\"County
        Subdivisions (COUSUB)\",\"description\":\"This file contains [County Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_11),
        which are the primary divisions of counties and equivalent entities. These
        divisions vary from state to state and include Barrios, Purchases, Townships,
        and other types of legal and statistical entities. \",\"msft:item_name\":\"cb_2020_us_cousub_500k\"},{\"name\":\"Divisions
        (DIVISION)\",\"description\":\"This file contains data on [Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10)
        of the US. This file is similar to the Regions file but contains more divisions
        and encompasses several states per division.\",\"msft:item_name\":\"cb_2020_us_division_500k\"},{\"name\":\"Core
        Based Statistical Areas (CBSAs)\",\"description\":\"This file contains data
        on [Core Based Statistical Areas (CBSAs)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7).
        This encompasses all metropolitan and micropolitan statistical areas.\",\"msft:item_name\":\"cb_2020_us_cbsa_500k\"},{\"name\":\"Combined
        Statistical Areas (CSA)\",\"description\":\"This file contains data on [Combined
        Statistical Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),
        which are areas that consist of two or more adjacent CBSAs that have significant
        employment interchanges.\",\"msft:item_name\":\"cb_2020_us_csa_500k\"},{\"name\":\"Metropolitan
        Divisions (METDIV)\",\"description\":\"This file contains data on [Metropolitan
        Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7).
        These areas are groupings of counties or equivalent entities within a metropolitan
        statistical area with a core of 2.5 million inhabitants and one or more main
        counties that represent employment centers, plus adjacent counties with commuting
        ties.\",\"msft:item_name\":\"cb_2020_us_metdiv_500k\"},{\"name\":\"New England
        City and Town Areas (NECTA)\",\"description\":\"This file contains [New England
        City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),
        which encompass metropolitan and micropolitan statistical areas and urban
        clusters in New England.\",\"msft:item_name\":\"cb_2020_us_necta_500k\"},{\"name\":\"New
        England City and Town Area Division (NECTADIV)\",\"description\":\"This file
        contains [New England City and Town Areas Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),
        which are smaller groupings of cities and towns in New England that contain
        a single core of 2.5 million inhabitants. Each division must have a total
        population of 100,000 or more.\",\"msft:item_name\":\"cb_2020_us_nectadiv_500k\"},{\"name\":\"Combined
        New England City and Town Areas (CNECTA)\",\"description\":\"This file contains
        data on [Combined New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),
        consisting of two or more adjacent NECTAs that have significant employment
        interchanges.\",\"msft:item_name\":\"cb_2020_us_cnecta_500k\"},{\"name\":\"Places
        (PLACE)\",\"description\":\"This file contains [Places](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_14)
        which are Incorporated Places (legal entities) and Census Designated Places
        (CDPs, statistical entities). An incorporated place usually is a city, town,
        village, or borough but can have other legal descriptions. CDPs are settled
        concentrations of population that are identifiable by name but are not legally
        incorporated.\",\"msft:item_name\":\"cb_2020_us_place_500k\"},{\"name\":\"Regions
        (REGION)\",\"description\":\"This file contains [Regions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10)
        of the US and encompasses several states per division.\",\"msft:item_name\":\"cb_2020_us_region_500k\"},{\"name\":\"School
        Districts - Elementary (ELSD)\",\"description\":\"This file contains [Elementary
        School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),
        referring to districts with elementary schools.\",\"msft:item_name\":\"cb_2020_us_elsd_500k\"},{\"name\":\"School
        Districts - Secondary (SCSD)\",\"description\":\"This file contains [Secondary
        School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),
        referring to districts with secondary schools.\",\"msft:item_name\":\"cb_2020_us_scsd_500k\"},{\"name\":\"School
        Districts - Unified (UNSD)\",\"description\":\"This file contains [Unified
        School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),
        referring to districts that provide education to children of all school ages.
        Unified school districts can have both secondary and elementary schools.\",\"msft:item_name\":\"cb_2020_us_unsd_500k\"},{\"name\":\"State
        Legislative Districts - Lower Chamber (SLDL)\",\"description\":\"This file
        contains [Lower Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).\",\"msft:item_name\":\"cb_2020_us_sldl_500k\"},{\"name\":\"State
        Legislative Districts - Upper Chamber (SLDU)\",\"description\":\"This file
        contains [Upper Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).\",\"msft:item_name\":\"cb_2020_us_sldu_500k\"},{\"name\":\"States
        (STATE)\",\"description\":\"This file contains the [US States and State Equivalent
        Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_25).
        Within Census Bureau datasets, the District of Columbia, Puerto Rico, and
        the Island Areas (American Samoa, the Commonwealth of the Northern Mariana
        Islands, Guam, and the US Virgin Islands) are treated as statistical equivalents
        of states alongside the 50 US states.\",\"msft:item_name\":\"cb_2020_us_state_500k\"},{\"name\":\"Subbarrios
        (SUBBARRIO)\",\"description\":\"This file contains [Subbarrios](https://www.census.gov/programs-surveys/geography/about/glossary.html#pr),
        which are legally defined subdivisions of Minor Civil Division in Puerto Rico.
        They don\\\"t exist within every Minor Civil Division and don\\\"t always
        cover the entire Minor Civil Division where they do exist.\",\"msft:item_name\":\"cb_2020_72_subbarrio_500k\"},{\"name\":\"United
        States Outline\",\"description\":\"This file contains the [United States Outline](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_30)
        shapefile. This contains all 50 US states plus the District of Columbia, Puerto
        Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern
        Mariana Islands, Guam, and the US Virgin Islands). There is only one feature
        within this dataset.\",\"msft:item_name\":\"cb_2020_us_nation_5m\"},{\"name\":\"Voting
        Districts (VTD)\",\"description\":\"This file contains all [US Voting Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_31),
        which are geographic features established by state, local and tribal governments
        to conduct elections.\",\"msft:item_name\":\"cb_2020_us_vtd_500k\"}],\"msft:container\":\"us-census\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"United
        States population counts at various levels of cartographic aggregation\",\"msft:region\":\"westeurope\"},{\"id\":\"jrc-gsw\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.1038/nature20584\"},{\"rel\":\"license\",\"href\":\"https://www.copernicus.eu/en/about-copernicus/international-cooperation\",\"title\":\"Copernicus
        Open Access Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/jrc-gsw\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"JRC
        Global Surface Water\",\"assets\":{\"guide\":{\"href\":\"https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf\",\"type\":\"application/pdf\",\"roles\":[\"metadata\"],\"title\":\"Users
        Guide\",\"description\":\"Data users guide and description of the JRC GSW
        datasets\"},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/jrc-gsw.png\",\"title\":\"European
        Commission Joint Research Centre - Global Surface Water\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/jrc-gsw.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-56.0,180.0,78.0]]},\"temporal\":{\"interval\":[[\"1984-03-01T00:00:00Z\",\"2020-12-31T11:59:59Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.1038/nature20584\",\"keywords\":[\"Global\",\"Water\",\"Landsat\"],\"providers\":[{\"url\":\"https://global-surface-water.appspot.com/\",\"name\":\"European
        Commission Joint Research Centre\",\"roles\":[\"producer\",\"processor\"]},{\"url\":\"https://developers.google.com/earth-engine/datasets/tags/jrc\",\"name\":\"Google\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"Global
        surface water products from the European Commission Joint Research Centre,
        based on Landsat 5, 7, and 8 imagery.  Layers in this collection describe
        the occurrence, change, and seasonality of surface water from 1984-2020.  Complete
        documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\\n\",\"item_assets\":{\"change\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Occurrence
        Change Intensity\",\"description\":\"Change in water occurrence between the
        two periods (1984-1999) and (2000-2020)\"},\"extent\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Maximum
        Water Extent\",\"description\":\"Binary indicator of whether water was ever
        present (from 1984-2020)\",\"file:values\":[{\"values\":[0],\"summary\":\"Not
        water\"},{\"values\":[1],\"summary\":\"Water\"}]},\"occurrence\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Occurrence\",\"description\":\"Frequency
        with which water was present from March 1984 to December 2020\"},\"recurrence\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Recurrence\",\"description\":\"Frequency
        with which water returns from year to year\"},\"seasonality\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Seasonality\",\"description\":\"Number
        of months that water was present from January 2020 to December 2020\"},\"transitions\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Transitions\",\"description\":\"Categorical
        change in surface water status from 1984 to 2020\",\"file:values\":[{\"values\":[1],\"summary\":\"Permanent\"},{\"values\":[2],\"summary\":\"New
        permanent\"},{\"values\":[3],\"summary\":\"Lost permanent\"},{\"values\":[4],\"summary\":\"Seasonal\"},{\"values\":[5],\"summary\":\"New
        seasonal\"},{\"values\":[6],\"summary\":\"Lost seasonal\"},{\"values\":[7],\"summary\":\"Seasonal
        to permanent\"},{\"values\":[8],\"summary\":\"Permanent to seasonal\"},{\"values\":[9],\"summary\":\"Ephemeral
        permanent\"},{\"values\":[10],\"summary\":\"Ephemeral seasonal\"}]}},\"sci:citation\":\"Jean-Francois
        Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping
        of global surface water and its long-term changes. Nature 540, 418-422 (2016)\",\"stac_version\":\"1.0.0\",\"msft:container\":\"jrc-gsw\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Global
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        Guide\"},{\"rel\":\"license\",\"href\":\"https://cdla.dev/permissive-1-0/\",\"type\":\"text/html\",\"title\":\"Community
        Data License Agreement - Permissive, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/deltares-floods\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Deltares
        Global Flood Maps\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/deltares-flood.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,90.0,180.0,-90.0]]},\"temporal\":{\"interval\":[[\"2018-01-01T00:00:00Z\",\"2018-12-31T00:00:00Z\"],[\"2050-01-01T00:00:00Z\",\"2050-12-31T00:00:00Z\"]]}},\"license\":\"CDLA-Permissive-1.0\",\"keywords\":[\"Deltares\",\"Flood\",\"Sea
        level rise\",\"Water\",\"Global\"],\"providers\":[{\"url\":\"https://www.deltares.nl/en/\",\"name\":\"Deltares\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"deltares:dem_name\":[\"NASADEM\",\"MERITDEM\",\"LIDAR\"],\"deltares:resolution\":[\"90m\",\"1km\",\"5km\"],\"deltares:return_period\":[0,2,5,10,25,50,100,250],\"deltares:sea_level_year\":[2018,2050]},\"description\":\"[Deltares](https://www.deltares.nl/en/)
        has produced inundation maps of flood depth using a model that takes into
        account water level attenuation and is forced by sea level. At the coastline,
        the model is forced by extreme water levels containing surge and tide from
        GTSMip6. The water level at the coastline is extended landwards to all areas
        that are hydrodynamically connected to the coast following a \u2018bathtub\u2019
        like approach and calculates the flood depth as the difference between the
        water level and the topography. Unlike a simple 'bathtub' model, this model
        attenuates the water level over land with a maximum attenuation factor of
        0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the
        flood levels due to the roughness over land.\\n\\nIn its current version,
        the model does not account for varying roughness over land and permanent water
        bodies such as rivers and lakes, and it does not account for the compound
        effects of waves, rainfall, and river discharge on coastal flooding. It also
        does not include the mitigating effect of coastal flood protection. Flood
        extents must thus be interpreted as the area that is potentially exposed to
        flooding without coastal protection.\\n\\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf)
        for more information.\\n\\n## Digital elevation models (DEMs)\\n\\nThis documentation
        will refer to three DEMs:\\n\\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem)
        product.\\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/),
        derived from SRTM and AW3D.\\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\\n\\n##
        Global datasets\\n\\nThis collection includes multiple global flood datasets
        derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different
        resolutions. Not all DEMs have all resolutions:\\n\\n* `NASADEM` and `MERITDEM`
        are available at `90m` and `1km` resolutions\\n* `LIDAR` is available at `5km`
        resolution\\n\\n## Historic event datasets\\n\\nThis collection also includes
        historical storm event data files that follow similar DEM and resolution conventions.
        Not all storms events are available for each DEM and resolution combination,
        but generally follow the format of:\\n\\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\\n\\nFor
        example, a flood map for the MERITDEM-derived 90m flood data for the \\\"Omar\\\"
        storm in 2008 is available at:\\n\\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\\n\\n##
        Contact\\n\\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).\",\"item_assets\":{\"data\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Flood
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        to as the continuity index to the existing National Oceanic and Atmospheric
        Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived
        NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI),
        which has improved sensitivity over high biomass regions. The algorithm for
        this product chooses the best available pixel value from all the acquisitions
        from the 16 day period. The criteria used is low clouds, low view angle, and
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        for North America and are distributed in Zarr and NetCDF formats, compliant
        with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\\n\\nUse
        the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840)
        to cite your usage of the data.\\n\\nThis dataset provides coverage for Hawaii;
        North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\\n\\n\",\"sci:citation\":\"Thornton,
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        as produced by Natural Resources Canada using Landsat satellite imagery. This
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        lands\"},{\"values\":[17],\"summary\":\"Urban\"},{\"values\":[18],\"summary\":\"Water\"},{\"values\":[19],\"summary\":\"Snow
        and Ice\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"landcover\",\"stac_extensions\":[\"https://stac-extensions.github.io/label/v1.0.0/schema.json\",\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"nrcanlandcovereuwest\",\"msft:short_description\":\"Collection
        of Land Cover products for Canada as produced by Natural Resources Canada
        using Landsat satellite imagery. This collection of cartographic products
        offers classified Land Cover of Canada at a 30 metre scale, updated on a 5
        year basis.\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-10A2-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD10A2.061\",\"title\":\"MODIS/Terra
        Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD10A2.061\",\"title\":\"MODIS/Aqua
        Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61\"},{\"rel\":\"license\",\"href\":\"https://nsidc.org/data/data-programs/nsidc-daac/citing-nsidc-daac\",\"title\":\"Use
        and Copyright | National Snow and Ice Data Center\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-10A2-061\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS
        Snow Cover 8-day\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-10A2-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS
        Snow Cover 8-day thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-10A2-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-02-18T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Global\",\"Snow\",\"MOD10A2\",\"MYD10A2\"],\"providers\":[{\"url\":\"https://nsidc.org\",\"name\":\"National
        Snow and Ice Data Center\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"platform\":[\"aqua\",\"terra\"],\"instruments\":[\"modis\"]},\"description\":\"This
        global Level-3 (L3) data set provides the maximum snow cover extent observed
        over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles
        are generated by compositing 500 m observations from the 'MODIS Snow Cover
        Daily L3 Global 500m Grid' data set. A bit flag index is used to track the
        eight-day snow/no-snow chronology for each 500 m cell.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source
        data containing all bands\"},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal
        Geographic Data Committee (FGDC) Metadata\"},\"Maximum_Snow_Extent\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Maximum
        snow extent over the 8-day period.\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":500}],\"classification:classes\":[{\"value\":0,\"description\":\"missing
        data\"},{\"value\":1,\"description\":\"no decision\"},{\"value\":11,\"description\":\"night\"},{\"value\":25,\"description\":\"no
        snow\"},{\"value\":37,\"description\":\"lake\"},{\"value\":39,\"description\":\"ocean\"},{\"value\":50,\"description\":\"cloud\"},{\"value\":100,\"description\":\"lake
        ice\"},{\"value\":200,\"description\":\"snow\"},{\"value\":254,\"description\":\"detector
        saturated\"}]},\"Eight_Day_Snow_Cover\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Snow chronology
        bit flags for each day in the eight-day observation period.\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":500}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"modis\",\"msft:container\":\"modis-061\",\"stac_extensions\":[\"https://stac-extensions.github.io/classification/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.5067/MODIS/MOD10A2.061\",\"citation\":\"Hall,
        D. K., &amp; Riggs, G. A. (2021). <i>MODIS/Terra Snow Cover 8-Day L3 Global
        500m Grid, Version 61</i> [Data set]. NASA National Snow and Ice Data Center
        DAAC. https://doi.org/10.5067/MODIS/MOD10A2.061\"},{\"doi\":\"10.5067/MODIS/MYD10A2.061\",\"citation\":\"Hall,
        D. K., &amp; Riggs, G. A. (2021). <i>MODIS/Aqua Snow Cover 8-Day L3 Global
        500m Grid, Version 61</i> [Data set]. NASA National Snow and Ice Data Center
        DAAC. https://doi.org/10.5067/MODIS/MYD10A2.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS
        Snow Cover 8-day\",\"msft:region\":\"westeurope\"},{\"id\":\"ecmwf-forecast\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC-BY-4.0
        license\"},{\"rel\":\"documentation\",\"href\":\"https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time\",\"type\":\"text/html\",\"title\":\"ECMWF
        Open Data (Real Time) documentation\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ECMWF
        Open Data (real-time)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/ecmwf-forecast-thumbnail-proj.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-180,90,180,-90]]},\"temporal\":{\"interval\":[[null,null]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"ECMWF\",\"forecast\",\"weather\"],\"providers\":[{\"url\":\"https://www.ecmwf.int/\",\"name\":\"ECMWF\",\"roles\":[\"producer\"]}],\"summaries\":{\"ecmwf:types\":[\"ef\",\"ep\",\"fc\",\"tf\"],\"ecmwf:streams\":[\"enfo\",\"mmsf\",\"oper\",\"scda\",\"scwv\",\"waef\",\"wave\"],\"ecmwf:pressure_levels\":[1000,925,850,700,500,300,250,200,50],\"ecmwf:reference_times\":[\"00\",\"06\",\"12\",\"18\"]},\"description\":\"The
        [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products)
        offers real-time meterological and oceanographic productions from the ECMWF
        forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction)
        for detailed information on each of the products.\\n\\n## Overview of products\\n\\nThe
        following diagram shows the publishing schedule of the various products.\\n\\n<a
        href=\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\\\"><img
        src=\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\\\"
        width=\\\"100%\\\"/></a>\\n\\nThe vertical axis shows the various products,
        defined below, which are grouped by combinations of `stream`, `forecast type`,
        and `reference time`. The horizontal axis shows *forecast times* in 3-hour
        intervals out from the reference time. A black square over a particular forecast
        time, or step, indicates that a forecast is made for that forecast time, for
        that particular `stream`, `forecast type`, `reference time` combination.\\n\\n*
        **stream** is the forecasting system that produced the data. The values are
        available in the `ecmwf:stream` summary of the STAC collection. They are:\\n
        \ * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts),
        atmospheric fields\\n  * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast)
        fields from the ECMWF model only.\\n  * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast),
        atmospheric fields \\n  * `scda`: short cut-off high-resolution forecast,
        atmospheric fields (also known as \\\"high-frequency products\\\")\\n  * `scwv`:
        short cut-off high-resolution forecast, ocean wave fields (also known as \\\"high-frequency
        products\\\") and\\n  * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts),
        ocean wave fields,\\n  * `wave`: wave model\\n* **type** is the forecast type.
        The values are available in the `ecmwf:type` summary of the STAC collection.
        They are:\\n  * `fc`: forecast\\n  * `ef`: ensemble forecast\\n  * `pf`: ensemble
        probabilities\\n  * `tf`: trajectory forecast for tropical cyclone tracks\\n*
        **reference time** is the hours after midnight when the model was run. Each
        stream / type will produce assets for different forecast times (steps from
        the reference datetime) depending on the reference time.\\n\\nVisit the [ECMWF's
        User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time)
        for more details on each of the various products.\\n\\nAssets are available
        for the previous 30 days.\\n\\n## Asset overview\\n\\nThe data are provided
        as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\\nAdditionally,
        [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles)
        are provided, which can be used to read subsets of the data from Azure Blob
        Storage.\\n\\nWithin each `stream`, `forecast type`, `reference time`, the
        structure of the data are mostly consistent. Each GRIB2 file will have the\\nsame
        data variables, coordinates (aside from `time` as the *reference time* changes
        and `step` as the *forecast time* changes). The exception\\nis the `enfo-ep`
        and `waef-ep` products, which have more `step`s in the 240-hour forecast than
        in the 360-hour forecast. \\n\\nSee the example notebook for more on how to
        access the data.\\n\\n## STAC metadata\\n\\nThe Planetary Computer provides
        a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so
        every item has the same\\n`bbox` and `geometry`.\\n\\nA few custom properties
        are available on each STAC item, which can be used in searches to narrow down
        the data to items of interest:\\n\\n* `ecmwf:stream`: The forecasting system
        (see above for definitions). The full set of values is available in the Collection's
        summaries.\\n* `ecmwf:type`: The forecast type (see above for definitions).
        The full set of values is available in the Collection's summaries.\\n* `ecmwf:step`:
        The offset from the reference datetime, expressed as `<value><unit>`, for
        example `\\\"3h\\\"` means \\\"3 hours from the reference datetime\\\". \\n*
        `ecmwf:reference_datetime`: The datetime when the model was run. This indicates
        when the forecast *was made*, rather than when it's valid for.\\n* `ecmwf:forecast_datetime`:
        The datetime for which the forecast is valid. This is also set as the item's
        `datetime`.\\n\\nSee the example notebook for more on how to use the STAC
        metadata to query for particular data.\\n\\n## Attribution\\n\\nThe products
        listed and described on this page are available to the public and their use
        is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms
        of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that
        the data may be redistributed and used commercially, subject to appropriate
        attribution.\\n\\nThe following wording should be attached to the use of this
        ECMWF dataset: \\n\\n1. Copyright statement: Copyright \\\"\xA9 [year] European
        Centre for Medium-Range Weather Forecasts (ECMWF)\\\".\\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\\n3.
        License Statement: This data is published under a Creative Commons Attribution
        4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\\n4.
        Disclaimer: ECMWF does not accept any liability whatsoever for any error or
        omission in the data, their availability, or for any loss or damage arising
        from their use.\\n5. Where applicable, an indication if the material has been
        modified and an indication of previous modifications.\\n\\nThe following wording
        shall be attached to services created with this ECMWF dataset:\\n\\n1. Copyright
        statement: Copyright \\\"This service is based on data and products of the
        European Centre for Medium-Range Weather Forecasts (ECMWF)\\\".\\n2. Source
        www.ecmwf.int\\n3. License Statement: This ECMWF data is published under a
        Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\\n4.
        Disclaimer: ECMWF does not accept any liability whatsoever for any error or
        omission in the data, their availability, or for any loss or damage arising
        from their use.\\n5. Where applicable, an indication if the material has been
        modified and an indication of previous modifications\\n\\n## More information\\n\\nFor
        more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time)
        and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).\",\"item_assets\":{\"data\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"GRIB2
        data file\",\"description\":\"The forecast data, as a grib2 file. Subsets
        of the data can be loaded using information from the associated index file.\"},\"index\":{\"type\":\"application/x-ndjson\",\"roles\":[\"index\"],\"title\":\"Index
        file\",\"description\":\"The index file contains information on each message
        within the GRIB2 file.\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"ecmwf\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"ECMWF
        Open Data (Real Time) forecasts\"},{\"id\":\"noaa-mrms-qpe-24h-pass2\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2\"},{\"rel\":\"license\",\"href\":\"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf\",\"type\":\"application/pdf\",\"title\":\"MRMS
        Dataset Sharing Policy\"},{\"rel\":\"about\",\"href\":\"https://mrms.nssl.noaa.gov\",\"type\":\"text/html\",\"title\":\"MRMS
        Homepage\"},{\"rel\":\"about\",\"href\":\"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881\",\"type\":\"text/html\",\"title\":\"MRMS
        QPE Technical Product Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-24h-pass2\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA
        MRMS QPE 24-Hour Pass 2\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-24h-pass2-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NOAA
        MRMS QPE 24-Hour Pass 2 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-mrms-qpe-24h-pass2.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-176.0,9.0,150.0,72.0],[-130.0,20.0,-60.0,55.0],[-164.0,15.0,-151.0,26.0],[140.0,9.0,150.0,18.0],[-176.0,50.0,-126.0,72.0],[-90.0,10.0,-60.0,25.0]]},\"temporal\":{\"interval\":[[\"2022-07-21T20:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NOAA\",\"MRMS\",\"QPE\",\"Precipitation\",\"Weather\",\"United
        States\",\"Guam\",\"Caribbean\"],\"providers\":[{\"url\":\"https://www.nssl.noaa.gov/projects/mrms\",\"name\":\"NOAA
        National Severe Storms Laboratory\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"noaa_mrms_qpe:pass\":[2],\"noaa_mrms_qpe:period\":[24],\"noaa_mrms_qpe:region\":[\"CONUS\",\"HAWAII\",\"GUAM\",\"ALASKA\",\"CARIB\"]},\"description\":\"The
        [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/)
        products are seamless 1-km mosaics of precipitation accumulation covering
        the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The
        products are automatically generated through integration of data from multiple
        radars and radar networks, surface and satellite observations, numerical weather
        prediction (NWP) models, and climatology. The products are updated hourly
        at the top of the hour.\\n\\nMRMS QPE is available as a \\\"Pass 1\\\" or
        \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency
        and includes 60-65% of gauges. The Pass 2 product has a higher latency of
        120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are
        broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis
        Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative
        precipitation accumulation with a 2-hour latency. The data are available in
        [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original
        source GRIB2 format files. The GRIB2 files are delivered to Azure as part
        of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\",\"item_assets\":{\"cog\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Processed
        Cloud Optimized GeoTIFF file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]},\"grib2\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"Original
        GRIB2 file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-mrms-qpe\",\"msft:container\":\"mrms-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json\"],\"msft:storage_account\":\"mrms\",\"msft:short_description\":\"Integrated
        multi-sensor cumulative precipitation estimate for the past 24 hours with
        a 2-hour latency.\",\"msft:region\":\"westeurope\"},{\"id\":\"nasadem\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"Public
        Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/nasadem\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NASADEM
        HGT v001\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/nasadem.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NASADEM\"},\"geoparquet-items\":{\"href\":\"abfs://items/nasadem.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-179.000139,-56.000139,179.000139,61.000139]]},\"temporal\":{\"interval\":[[\"2000-02-20T00:00:00Z\",\"2000-02-20T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"JPL\",\"Elevation\",\"DEM\",\"USGS\",\"NGA\",\"SRTM\"],\"providers\":[{\"url\":\"https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem\",\"name\":\"NASA\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://trs.jpl.nasa.gov/handle/2014/46123\",\"name\":\"JPL\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://lpdaac.usgs.gov/products/nasadem_hgtv001/\",\"name\":\"USGS\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://doi.org/10.5069/G93T9FD9\",\"name\":\"OpenTopography\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[30]},\"description\":\"[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem)
        provides global topographic data at 1 arc-second (~30m) horizontal resolution,
        derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/)
        (SRTM).\\n\\n\",\"item_assets\":{\"elevation\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Elevation\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"nasadem-cog\",\"stac_extensions\":[\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"nasademeuwest\",\"msft:short_description\":\"Global
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        from data captured via the Shuttle Radar Topography Mission\",\"msft:region\":\"westeurope\"},{\"id\":\"io-lulc\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc\"},{\"rel\":\"related\",\"href\":\"https://livingatlas.arcgis.com/landcover/\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC
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        _A new version of this item is available for your use. This mature version
        of the map remains available for use in existing applications. This item will
        be retired in December 2024. There is 2020 data available in the newer [9-class
        dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\\n\\nGlobal
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        Sentinel-2 imagery at 10m resolution.  This dataset was generated by [Impact
        Observatory](http://impactobservatory.com/), who used billions of human-labeled
        pixels (curated by the National Geographic Society) to train a deep learning
        model for land classification.  The global map was produced by applying this
        model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\\n\\nThis
        dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
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        data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product)
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        the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2)
        acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/)
        onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013.
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        Landsat 1 through Landsat 5.\",\"msft:region\":\"westeurope\"},{\"id\":\"drcog-lulc\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/drcog-lulc/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/drcog-lulc\"},{\"rel\":\"license\",\"href\":\"https://drcog.org/legal-terms\",\"type\":\"text/html\",\"title\":\"DRCOG
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        of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project)
        datasets are developed in partnership with the [Babbit Center for Land and
        Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy)
        and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s
        Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft
        (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution
        covering 6,000 square miles of the Denver, Colorado region. The classification
        data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP)
        aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver
        Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project)
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        and 2020 Land Use/Land Cover classification of the Denver, Colorado region.\",\"msft:region\":\"westeurope\"},{\"id\":\"chesapeake-lc-7\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-7/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-7\"},{\"rel\":\"describedby\",\"href\":\"https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf\",\"type\":\"application/pdf\",\"title\":\"User
        Guide\"},{\"rel\":\"license\",\"href\":\"https://www.chesapeakeconservancy.org/conservation-innovation-center/precision-conservation/chesapeake-bay-program/\",\"type\":\"text/html\",\"title\":\"Data
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        The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)
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        for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is
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        imagery from the National Agriculture Imagery Program (NAIP). The product
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        Maryland, Delaware, West Virginia, Virginia, and the District of Columbia.
        The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)
        [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/)
        for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is
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        and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf)
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        created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/)
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        landfills, floodplains, and wetlands. The product area encompasses over 250,000
        square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia,
        Virginia, and the District of Columbia. The dataset was created by the [Chesapeake
        Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation
        Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/)
        for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is
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        Chesapeake Bay restoration efforts.\\n\\nThe dataset is composed of 17 land
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        information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf)
        document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/)
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        [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/)
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        products are automatically generated through integration of data from multiple
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        \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency
        and includes 60-65% of gauges. The Pass 2 product has a higher latency of
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        broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis
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        of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\",\"item_assets\":{\"cog\":{\"type\":\"image/tiff;
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        products are seamless 1-km mosaics of precipitation accumulation covering
        the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The
        products are automatically generated through integration of data from multiple
        radars and radar networks, surface and satellite observations, numerical weather
        prediction (NWP) models, and climatology. The products are updated hourly
        at the top of the hour.\\n\\nMRMS QPE is available as a \\\"Pass 1\\\" or
        \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency
        and includes 60-65% of gauges. The Pass 2 product has a higher latency of
        120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are
        broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis
        Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative
        precipitation accumulation with a 2-hour latency. The data are available in
        [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original
        source GRIB2 format files. The GRIB2 files are delivered to Azure as part
        of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\",\"item_assets\":{\"cog\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Processed
        Cloud Optimized GeoTIFF file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]},\"grib2\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"Original
        GRIB2 file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-mrms-qpe\",\"msft:container\":\"mrms-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json\"],\"msft:storage_account\":\"mrms\",\"msft:short_description\":\"Integrated
        multi-sensor cumulative precipation estimate for the past hour with a 2-hour
        latency.\",\"msft:region\":\"westeurope\"},{\"id\":\"noaa-nclimgrid-monthly\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.7289/V5SX6B56\",\"type\":\"text/html\",\"title\":\"NOAA
        Monthly U.S. Climate Gridded Dataset (NClimGrid)\"},{\"rel\":\"license\",\"href\":\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332#Constraints\",\"type\":\"text/html\",\"title\":\"NClimGrid
        Data Use and Access Constraints\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332\",\"type\":\"text/html\",\"title\":\"Product
        Landing Page\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-nclimgrid-monthly\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Monthly
        NOAA U.S. Climate Gridded Dataset (NClimGrid)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-nclimgrid-monthly-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Monthly
        NOAA NClimGrid Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-nclimgrid-monthly.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.708333,24.541666,-66.999995,49.375001]]},\"temporal\":{\"interval\":[[\"1895-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.7289/V5SX6B56\",\"keywords\":[\"United
        States\",\"NOAA\",\"NClimGrid\",\"Climate\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA
        National Centers for Environmental Information\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"The
        [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332)
        consists of four climate variables derived from the [Global Historical Climatology
        Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily)
        dataset: maximum temperature, minimum temperature, average temperature, and
        precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer)
        grids for the Continental United States (CONUS). \\n\\nNClimGrid data is available
        in monthly and daily temporal intervals, with the daily data further differentiated
        as \\\"prelim\\\" (preliminary) or \\\"scaled\\\". Preliminary daily data
        is available within approximately three days of collection. Once a calendar
        month of preliminary daily data has been collected, it is scaled to match
        the corresponding monthly value. Monthly data is available from 1895 to the
        present. Daily preliminary and daily scaled data is available from 1951 to
        the present. \\n\\nThis Collection contains **Monthly** data. See the journal
        publication [\\\"Improved Historical Temperature and Precipitation Time Series
        for U.S. Climate Divisions\\\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml)
        for more information about monthly gridded data.\\n\\nUsers of all NClimGrid
        data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332)
        that:\\n>\\\"On an annual basis, approximately one year of 'final' NClimGrid
        data is submitted to replace the initially supplied 'preliminary' data for
        the same time period. Users should be sure to ascertain which level of data
        is required for their research.\\\"\\n\\nThe source NetCDF files are delivered
        to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\n*Note*:
        The Planetary Computer currently has STAC metadata for just the monthly collection.
        We'll have STAC metadata for daily data in our next release. In the meantime,
        you can access the daily NetCDF data directly from Blob Storage using the
        storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`.
        See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns
        for more.*\\n\",\"item_assets\":{\"prcp\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly Precipitation
        (mm)\",\"raster:bands\":[{\"unit\":\"mm\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly
        Average Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly
        Maximmum Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly
        Minimum Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]}},\"sci:citation\":\"Vose,
        Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew
        J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek
        (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1.
        NOAA National Centers for Environmental Information. DOI:10.7289/V5SX6B56.\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-nclimgrid\",\"msft:container\":\"nclimgrid-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.1175/JAMC-D-13-0248.1\",\"citation\":\"Vose,
        R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C.
        N., Jr., Fenimore, C., Gleason, K., & Arndt, D. (2014). Improved Historical
        Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal
        of Applied Meteorology and Climatology, 53(5), 1232-1251.\"}],\"msft:storage_account\":\"nclimgridwesteurope\",\"msft:short_description\":\"Gridded
        surface observations of monthly temperature and precipitation for the continental
        United States\",\"msft:region\":\"westeurope\"},{\"id\":\"usda-cdl\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl\"},{\"rel\":\"about\",\"href\":\"https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php\",\"title\":\"Product
        Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#Section3_5.0\",\"title\":\"Redistribution
        information\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/usda-cdl\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USDA
        Cropland Data Layers (CDLs)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usda-cdl-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"USDA
        Cropland Data Layer (CDL) thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-127.887212,22.94027,-65.345507,51.603492]]},\"temporal\":{\"interval\":[[\"2008-01-01T00:00:00Z\",\"2021-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USDA\",\"United
        States\",\"Land Cover\",\"Land Use\",\"Agriculture\"],\"providers\":[{\"url\":\"https://www.nass.usda.gov/\",\"name\":\"United
        States Department of Agriculture - National Agricultural Statistics Service\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"usda_cdl:type\":[\"cropland\",\"frequency\",\"cultivated\"]},\"description\":\"The
        Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics
        Service (NASS) with the mission \\\"to provide timely, accurate and useful
        statistics in service to U.S. agriculture\\\" (Johnson and Mueller, 2010,
        p. 1204). The CDL is a crop-specific land cover classification product of
        more than 100 crop categories grown in the United States. CDLs are derived
        using a supervised land cover classification of satellite imagery. The supervised
        classification relies on first manually identifying pixels within certain
        images, often called training sites, which represent the same crop or land
        cover type. Using these training sites, a spectral signature is developed
        for each crop type that is then used by the analysis software to identify
        all other pixels in the satellite image representing the same crop. Using
        this method, a new CDL is compiled annually and released to the public a few
        months after the end of the growing season.\\n\\nThis collection includes
        Cropland, Confidence, Cultivated, and Frequency products.\\n\\n- Cropland:
        Crop-specific land cover data created annually. There are currently four individual
        crop frequency data layers that represent four major crops: corn, cotton,
        soybeans, and wheat.\\n- Confidence: The predicted confidence associated with
        an output pixel. A value of zero indicates low confidence, while a value of
        100 indicates high confidence.\\n- Cultivated: cultivated and non-cultivated
        land cover for CONUS based on land cover information derived from the 2017
        through 2021 Cropland products.\\n- Frequency: crop specific planting frequency
        based on land cover information derived from the 2008 through 2021 Cropland
        products.\\n\\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).\",\"item_assets\":{\"corn\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"wheat\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cotton\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cropland\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"value\":1,\"color_hint\":\"FFD200\",\"description\":\"Corn\"},{\"value\":2,\"color_hint\":\"FF2525\",\"description\":\"Cotton\"},{\"value\":3,\"color_hint\":\"00A8E3\",\"description\":\"Rice\"},{\"value\":4,\"color_hint\":\"FF9E0A\",\"description\":\"Sorghum\"},{\"value\":5,\"color_hint\":\"256F00\",\"description\":\"Soybeans\"},{\"value\":6,\"color_hint\":\"FFFF00\",\"description\":\"Sunflower\"},{\"value\":10,\"color_hint\":\"6FA400\",\"description\":\"Peanuts\"},{\"value\":11,\"color_hint\":\"00AE4A\",\"description\":\"Tobacco\"},{\"value\":12,\"color_hint\":\"DDA40A\",\"description\":\"Sweet
        Corn\"},{\"value\":13,\"color_hint\":\"DDA40A\",\"description\":\"Pop or Orn
        Corn\"},{\"value\":14,\"color_hint\":\"7DD2FF\",\"description\":\"Mint\"},{\"value\":21,\"color_hint\":\"E1007B\",\"description\":\"Barley\"},{\"value\":22,\"color_hint\":\"886153\",\"description\":\"Durum
        Wheat\"},{\"value\":23,\"color_hint\":\"D7B56B\",\"description\":\"Spring
        Wheat\"},{\"value\":24,\"color_hint\":\"A46F00\",\"description\":\"Winter
        Wheat\"},{\"value\":25,\"color_hint\":\"D59EBB\",\"description\":\"Other Small
        Grains\"},{\"value\":26,\"color_hint\":\"6F6F00\",\"description\":\"Winter
        Wheat/Soybeans\"},{\"value\":27,\"color_hint\":\"AC007B\",\"description\":\"Rye\"},{\"value\":28,\"color_hint\":\"9F5888\",\"description\":\"Oats\"},{\"value\":29,\"color_hint\":\"6F0048\",\"description\":\"Millet\"},{\"value\":30,\"color_hint\":\"D59EBB\",\"description\":\"Speltz\"},{\"value\":31,\"color_hint\":\"D1FF00\",\"description\":\"Canola\"},{\"value\":32,\"color_hint\":\"7D99FF\",\"description\":\"Flaxseed\"},{\"value\":33,\"color_hint\":\"D5D500\",\"description\":\"Safflower\"},{\"value\":34,\"color_hint\":\"D1FF00\",\"description\":\"Rape
        Seed\"},{\"value\":35,\"color_hint\":\"00AE4A\",\"description\":\"Mustard\"},{\"value\":36,\"color_hint\":\"FFA4E1\",\"description\":\"Alfalfa\"},{\"value\":37,\"color_hint\":\"A4F18B\",\"description\":\"Other
        Hay/Non Alfalfa\"},{\"value\":38,\"color_hint\":\"00AE4A\",\"description\":\"Camelina\"},{\"value\":39,\"color_hint\":\"D59EBB\",\"description\":\"Buckwheat\"},{\"value\":41,\"color_hint\":\"A800E3\",\"description\":\"Sugarbeets\"},{\"value\":42,\"color_hint\":\"A40000\",\"description\":\"Dry
        Beans\"},{\"value\":43,\"color_hint\":\"6F2500\",\"description\":\"Potatoes\"},{\"value\":44,\"color_hint\":\"00AE4A\",\"description\":\"Other
        Crops\"},{\"value\":45,\"color_hint\":\"B07DFF\",\"description\":\"Sugarcane\"},{\"value\":46,\"color_hint\":\"6F2500\",\"description\":\"Sweet
        Potatoes\"},{\"value\":47,\"color_hint\":\"FF6666\",\"description\":\"Misc.
        Vegs & Fruits\"},{\"value\":48,\"color_hint\":\"FF6666\",\"description\":\"Watermelons\"},{\"value\":49,\"color_hint\":\"FFCC66\",\"description\":\"Onions\"},{\"value\":50,\"color_hint\":\"FF6666\",\"description\":\"Cucumbers\"},{\"value\":51,\"color_hint\":\"00AE4A\",\"description\":\"Chick
        Peas\"},{\"value\":52,\"color_hint\":\"00DDAE\",\"description\":\"Lentils\"},{\"value\":53,\"color_hint\":\"53FF00\",\"description\":\"Peas\"},{\"value\":54,\"color_hint\":\"F1A277\",\"description\":\"Tomatoes\"},{\"value\":55,\"color_hint\":\"FF6666\",\"description\":\"Caneberries\"},{\"value\":56,\"color_hint\":\"00AE4A\",\"description\":\"Hops\"},{\"value\":57,\"color_hint\":\"7DD2FF\",\"description\":\"Herbs\"},{\"value\":58,\"color_hint\":\"E8BEFF\",\"description\":\"Clover/Wildflowers\"},{\"value\":59,\"color_hint\":\"AEFFDD\",\"description\":\"Sod/Grass
        Seed\"},{\"value\":60,\"color_hint\":\"00AE4A\",\"description\":\"Switchgrass\"},{\"value\":61,\"color_hint\":\"BEBE77\",\"description\":\"Fallow/Idle
        Cropland\"},{\"value\":63,\"color_hint\":\"92CC92\",\"description\":\"Forest\"},{\"value\":64,\"color_hint\":\"C5D59E\",\"description\":\"Shrubland\"},{\"value\":65,\"color_hint\":\"CCBEA2\",\"description\":\"Barren\"},{\"value\":66,\"color_hint\":\"FF00FF\",\"description\":\"Cherries\"},{\"value\":67,\"color_hint\":\"FF8EAA\",\"description\":\"Peaches\"},{\"value\":68,\"color_hint\":\"B9004F\",\"description\":\"Apples\"},{\"value\":69,\"color_hint\":\"6F4488\",\"description\":\"Grapes\"},{\"value\":70,\"color_hint\":\"007777\",\"description\":\"Christmas
        Trees\"},{\"value\":71,\"color_hint\":\"B09A6F\",\"description\":\"Other Tree
        Crops\"},{\"value\":72,\"color_hint\":\"FFFF7D\",\"description\":\"Citrus\"},{\"value\":74,\"color_hint\":\"B56F5B\",\"description\":\"Pecans\"},{\"value\":75,\"color_hint\":\"00A482\",\"description\":\"Almonds\"},{\"value\":76,\"color_hint\":\"E9D5AE\",\"description\":\"Walnuts\"},{\"value\":77,\"color_hint\":\"B09A6F\",\"description\":\"Pears\"},{\"value\":81,\"color_hint\":\"F1F1F1\",\"description\":\"Clouds/No
        Data\"},{\"value\":82,\"color_hint\":\"9A9A9A\",\"description\":\"Developed\"},{\"value\":83,\"color_hint\":\"4A6fA2\",\"description\":\"Water\"},{\"value\":87,\"color_hint\":\"7DB0B0\",\"description\":\"Wetlands\"},{\"value\":88,\"color_hint\":\"E8FFBE\",\"description\":\"Nonag/Undefined\"},{\"value\":92,\"color_hint\":\"00FFFF\",\"description\":\"Aquaculture\"},{\"value\":111,\"color_hint\":\"4A6FA2\",\"description\":\"Open
        Water\"},{\"value\":112,\"color_hint\":\"D2E1F8\",\"description\":\"Perennial
        Ice/Snow\"},{\"value\":121,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Open
        Space\"},{\"value\":122,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Low
        Intensity\"},{\"value\":123,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Med
        Intensity\"},{\"value\":124,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/High
        Intensity\"},{\"value\":131,\"color_hint\":\"CCBEA2\",\"description\":\"Barren\"},{\"value\":141,\"color_hint\":\"92CC92\",\"description\":\"Deciduous
        Forest\"},{\"value\":142,\"color_hint\":\"92CC92\",\"description\":\"Evergreen
        Forest\"},{\"value\":143,\"color_hint\":\"92CC92\",\"description\":\"Mixed
        Forest\"},{\"value\":152,\"color_hint\":\"C5D59E\",\"description\":\"Shrubland\"},{\"value\":176,\"color_hint\":\"E8FFBE\",\"description\":\"Grassland/Pasture\"},{\"value\":190,\"color_hint\":\"7DB0B0\",\"description\":\"Woody
        Wetlands\"},{\"value\":195,\"color_hint\":\"7DB0B0\",\"description\":\"Herbaceous
        Wetlands\"},{\"value\":204,\"color_hint\":\"00FF8B\",\"description\":\"Pistachios\"},{\"value\":205,\"color_hint\":\"D59EBB\",\"description\":\"Triticale\"},{\"value\":206,\"color_hint\":\"FF6666\",\"description\":\"Carrots\"},{\"value\":207,\"color_hint\":\"FF6666\",\"description\":\"Asparagus\"},{\"value\":208,\"color_hint\":\"FF6666\",\"description\":\"Garlic\"},{\"value\":209,\"color_hint\":\"FF6666\",\"description\":\"Cantaloupes\"},{\"value\":210,\"color_hint\":\"FF8EAA\",\"description\":\"Prunes\"},{\"value\":211,\"color_hint\":\"334833\",\"description\":\"Olives\"},{\"value\":212,\"color_hint\":\"E36F25\",\"description\":\"Oranges\"},{\"value\":213,\"color_hint\":\"FF6666\",\"description\":\"Honeydew
        Melons\"},{\"value\":214,\"color_hint\":\"FF6666\",\"description\":\"Broccoli\"},{\"value\":215,\"color_hint\":\"66994B\",\"description\":\"Avocados\"},{\"value\":216,\"color_hint\":\"FF6666\",\"description\":\"Peppers\"},{\"value\":217,\"color_hint\":\"B09A6F\",\"description\":\"Pomegranates\"},{\"value\":218,\"color_hint\":\"FF8EAA\",\"description\":\"Nectarines\"},{\"value\":219,\"color_hint\":\"FF6666\",\"description\":\"Greens\"},{\"value\":220,\"color_hint\":\"FF8EAA\",\"description\":\"Plums\"},{\"value\":221,\"color_hint\":\"FF6666\",\"description\":\"Strawberreis\"},{\"value\":222,\"color_hint\":\"FF6666\",\"description\":\"Squash\"},{\"value\":223,\"color_hint\":\"FF8EAA\",\"description\":\"Apricots\"},{\"value\":224,\"color_hint\":\"00AE4A\",\"description\":\"Vetch\"},{\"value\":225,\"color_hint\":\"FFD200\",\"description\":\"Winter
        Wheat/Corn\"},{\"value\":226,\"color_hint\":\"FFD200\",\"description\":\"Oats/Corn\"},{\"value\":227,\"color_hint\":\"FF6666\",\"description\":\"Lettuce\"},{\"value\":228,\"color_hint\":\"FF6666\",\"description\":\"Triticale/Corn\"},{\"value\":229,\"color_hint\":\"FF6666\",\"description\":\"Pumpkins\"},{\"value\":230,\"color_hint\":\"886153\",\"description\":\"Lettuce/Durum
        Wheat\"},{\"value\":231,\"color_hint\":\"FF6666\",\"description\":\"Lettuce/Cataloupe\"},{\"value\":232,\"color_hint\":\"FF2525\",\"description\":\"Lettuce/Cotton\"},{\"value\":233,\"color_hint\":\"A1007B\",\"description\":\"Lettuce/Barley\"},{\"value\":234,\"color_hint\":\"FF9E0A\",\"description\":\"Durum
        Wheat/Sorghum\"},{\"value\":235,\"color_hint\":\"FF9E0A\",\"description\":\"Barley/Sorghum\"},{\"value\":236,\"color_hint\":\"A46F00\",\"description\":\"Winter
        Wheat/Sorghum\"},{\"value\":237,\"color_hint\":\"FFD200\",\"description\":\"Barley/Corn\"},{\"value\":238,\"color_hint\":\"A46F00\",\"description\":\"Winter
        Wheat/Cotton\"},{\"value\":239,\"color_hint\":\"256F00\",\"description\":\"Soybeans/Cotton\"},{\"value\":240,\"color_hint\":\"256F00\",\"description\":\"Soybeans/Oats
        \"},{\"value\":241,\"color_hint\":\"FFD200\",\"description\":\"Corn/Soybeans\"},{\"value\":242,\"color_hint\":\"000099\",\"description\":\"Blueberries\"},{\"value\":243,\"color_hint\":\"FF6666\",\"description\":\"Cabbage\"},{\"value\":244,\"color_hint\":\"FF6666\",\"description\":\"Cauliflower\"},{\"value\":245,\"color_hint\":\"FF6666\",\"description\":\"Celery\"},{\"value\":246,\"color_hint\":\"FF6666\",\"description\":\"Radishes\"},{\"value\":247,\"color_hint\":\"FF6666\",\"description\":\"Turnips\"},{\"value\":248,\"color_hint\":\"FF6666\",\"description\":\"Eggplants\"},{\"value\":249,\"color_hint\":\"FF6666\",\"description\":\"Gourds\"},{\"value\":250,\"color_hint\":\"FF6666\",\"description\":\"Cranberries\"},{\"value\":254,\"color_hint\":\"256F00\",\"description\":\"Barley/Soybeans\"}]},\"soybeans\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"confidence\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cultivated\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"value\":1,\"color_hint\":\"000000\",\"description\":\"Non-Cultivated\"},{\"value\":2,\"color_hint\":\"006300\",\"description\":\"Cultivated\"}]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"usda-cdl\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\"],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"The
        UDA Cropland Data Layer is an annual raster, geo-referenced, crop-specific
        land cover data layer produced using satellite imagery and extensive agricultural
        ground truth collected during the current growing season.\",\"msft:region\":\"westeurope\"},{\"id\":\"eclipse\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse\"},{\"rel\":\"license\",\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Microsoft%20Project%20Eclipse%20API%20Terms%20of%20Use_Mar%202022.pdf\",\"type\":\"application/pdf\",\"title\":\"Terms
        of use\"},{\"rel\":\"cite-as\",\"href\":\"https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf\",\"type\":\"application/pdf\",\"title\":\"Eclipse:
        An End-to-End Platform for Low-Cost, Hyperlocal Environment Sensing in Cities\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/eclipse\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Urban
        Innovation Eclipse Sensor Data\",\"assets\":{\"data\":{\"href\":\"abfs://eclipse/Chicago/\",\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Full
        dataset\",\"description\":\"Full parquet dataset\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/eclipse-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Urban
        Innovation Chicago Sensors\"},\"geoparquet-items\":{\"href\":\"abfs://items/eclipse.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-87.94011408252348,41.64454312178303,-87.5241371038952,42.023038586147585]]},\"temporal\":{\"interval\":[[\"2021-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Eclipse\",\"PM25\",\"air
        pollution\"],\"providers\":[{\"url\":\"https://www.microsoft.com/en-us/research/urban-innovation-research/\",\"name\":\"Urban
        Innovation\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"The
        [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/)
        Network is a low-cost air quality sensing network for cities and a research
        project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/)
        at Microsoft Research.\\n\\nProject Eclipse currently includes over 100 locations
        in Chicago, Illinois, USA.\\n\\nThis network was deployed starting in July,
        2021, through a collaboration with the City of Chicago, the Array of Things
        Project, JCDecaux Chicago, and the Environmental Law and Policy Center as
        well as local environmental justice organizations in the city. [This talk](
        https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/)
        documents the network design and data calibration strategy.\\n\\n## Storage
        resources\\n\\nData are stored in [Parquet](https://parquet.apache.org/) files
        in Azure Blob Storage in the West Europe Azure region, in the following blob
        container:\\n\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\\n\\nWithin
        that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`,
        where `YYYY-MM-DD` corresponds to the date of the snapshot.\\nEach snapshot
        contains a sensor readings from the next 7-days in Parquet format starting
        with date on the folder name YYYY-MM-DD.\\nTherefore, the data files for the
        first snapshot are at\\n\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\\n\\nThe
        Parquet file schema is as described below. \\n\\n## Additional Documentation\\n\\nFor
        details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\\n\\n##
        License and attribution\\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022)
        [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing
        in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\\n\\n##
        Contact\\n\\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question)
        \\n\\n\\n## Learn more\\n\\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/)
        contains an overview of the Project Eclipse at Microsoft Research.\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Weekly
        dataset\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"sci:citation\":\"Daepp,
        Cabral, Ranganathan et al. (2022) Eclipse: An End-to-End Platform for Low-Cost,
        Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing
        in Sensor Networks. Milan, Italy. Eclipse: An End-to-End Platform for Low-Cost,
        Hyperlocal Environmental Sensing in Cities\",\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"City\",\"type\":\"byte_array\",\"description\":\"City
        where the Microsoft Eclipse device is deployed\"},{\"name\":\"DeviceId\",\"type\":\"int32\",\"description\":\"Id
        for a given device\"},{\"name\":\"LocationName\",\"type\":\"byte_array\",\"description\":\"Unique
        string describing the device location\"},{\"name\":\"Latitude\",\"type\":\"double\",\"description\":\"Latitude
        of the device location\"},{\"name\":\"Longitude\",\"type\":\"double\",\"description\":\"Longitude
        of the device location\"},{\"name\":\"ReadingDateTimeUTC\",\"type\":\"int96\",\"description\":\"The
        UTC date time string (like 2022-03-04 20:27:25.000) when the reading from
        the Eclipse sensor was recorded\"},{\"name\":\"PM25\",\"type\":\"double\",\"description\":\"Uncalibrated
        Fine particulate matter (PM 2.5) in \xB5g/m\xB3\"},{\"name\":\"CalibratedPM25\",\"type\":\"double\",\"description\":\"Calibrated
        PM 2.5 in \xB5g/m\xB3\"},{\"name\":\"CalibratedO3\",\"type\":\"double\",\"description\":\"Calibrated
        Ozone in PPB\"},{\"name\":\"CalibratedNO2\",\"type\":\"double\",\"description\":\"Calibrated
        Nitrogen Dioxide in PPB\"},{\"name\":\"CO\",\"type\":\"double\",\"description\":\"Uncalibrated
        Carbon monoxide (CO) in PPM\"},{\"name\":\"Temperature\",\"type\":\"double\",\"description\":\"Degree
        Celsius\"},{\"name\":\"Humidity\",\"type\":\"double\",\"description\":\"Relative
        humidity\"},{\"name\":\"BatteryLevel\",\"type\":\"double\",\"description\":\"Device
        battery level in Volts\"},{\"name\":\"PercentBattery\",\"type\":\"double\",\"description\":\"Percent
        of device battery\"},{\"name\":\"CellSignal\",\"type\":\"double\",\"description\":\"Cellular
        signal strength in dB\"}],\"msft:container\":\"eclipse\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"A
        network of low-cost air quality sensing network for cities and led by the
        Urban Innovation Group at Microsoft Research\",\"msft:region\":\"westeurope\"},{\"id\":\"esa-cci-lc\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/satellite-land-cover.pdf\",\"type\":\"text/html\",\"title\":\"ESA
        CCI license\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\",\"type\":\"text/html\",\"title\":\"COPERNICUS
        license\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/vito-proba-v.pdf\",\"type\":\"text/html\",\"title\":\"VITO
        License\"},{\"rel\":\"about\",\"href\":\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover\",\"type\":\"text/html\",\"title\":\"Product
        Landing Page\"},{\"rel\":\"about\",\"href\":\"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        user guide for version 2.1\"},{\"rel\":\"about\",\"href\":\"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        user guide for version 2.0\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.24381/cds.006f2c9a\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/esa-cci-lc\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ESA
        Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/esa-cci-lc-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"ESA
        CCI Land Cover COGs Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/esa-cci-lc.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-90.0,180.0,90.0]]},\"temporal\":{\"interval\":[[\"1992-01-01T00:00:00Z\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.24381/cds.006f2c9a\",\"keywords\":[\"Land
        Cover\",\"ESA\",\"CCI\",\"Global\"],\"providers\":[{\"url\":\"https://vito.be\",\"name\":\"VITO\",\"roles\":[\"licensor\"],\"description\":\"Provides
        the PROBA-V source data (for v2.0).\"},{\"url\":\"https://uclouvain.be\",\"name\":\"UCLouvain\",\"roles\":[\"producer\"],\"description\":\"UCLouvain
        produces the dataset (v2.1) for the ESA Climate Change Initiative.\"},{\"url\":\"https://brockmann-consult.de\",\"name\":\"Brockmann
        Consult\",\"roles\":[\"processor\"],\"description\":\"Brockmann Consult is
        responsible for the required pre-processing and the distribution of the dataset
        (v2.1).\"},{\"url\":\"http://esa-landcover-cci.org\",\"name\":\"ESA Climate
        Change Initiative\",\"roles\":[\"licensor\"],\"description\":\"The ESA Climate
        Change Initiative (CCI) is leading the product creation.\"},{\"url\":\"https://copernicus.eu\",\"name\":\"Copernicus\",\"roles\":[\"licensor\"],\"description\":\"Hosts
        the data on the Copernicus Climate Data Store (CDS).\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"esa_cci_lc:version\":[\"2.0.7cds\",\"2.1.1\"]},\"description\":\"The
        ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview)
        provides consistent global annual land cover maps at 300m spatial resolution
        from 1992 to 2020. The land cover classes are defined using the United Nations
        Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/)
        (LCCS). In addition to the land cover maps, four quality flags are produced
        to document the reliability of the classification and change detection. \\n\\nThe
        data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf)
        to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\\n\",\"item_assets\":{\"lccs_class\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Land
        Cover Class Defined in the Land Cover Classification System\",\"description\":\"Land
        cover class per pixel, defined using the Land Cover Classification System
        developed by the United Nations Food and Agriculture Organization.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":300}],\"classification:classes\":[{\"name\":\"no-data\",\"value\":0,\"no_data\":true,\"color_hint\":\"000000\",\"description\":\"No
        data\"},{\"name\":\"cropland-1\",\"value\":10,\"color_hint\":\"FFFF64\",\"description\":\"Cropland,
        rainfed\"},{\"name\":\"cropland-1a\",\"value\":11,\"regional\":true,\"color_hint\":\"FFFF64\",\"description\":\"Cropland,
        rainfed, herbaceous cover\"},{\"name\":\"cropland-1b\",\"value\":12,\"regional\":true,\"color_hint\":\"FFFF00\",\"description\":\"Cropland,
        rainfed, tree, or shrub cover\"},{\"name\":\"cropland-2\",\"value\":20,\"color_hint\":\"AAF0F0\",\"description\":\"Cropland,
        irrigated or post-flooding\"},{\"name\":\"cropland-3\",\"value\":30,\"color_hint\":\"DCF064\",\"description\":\"Mosaic
        cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)\"},{\"name\":\"natural-veg\",\"value\":40,\"color_hint\":\"C8C864\",\"description\":\"Mosaic
        natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)\"},{\"name\":\"tree-1\",\"value\":50,\"color_hint\":\"006400\",\"description\":\"Tree
        cover, broadleaved, evergreen, closed to open (>15%)\"},{\"name\":\"tree-2\",\"value\":60,\"color_hint\":\"00A000\",\"description\":\"Tree
        cover, broadleaved, deciduous, closed to open (>15%)\"},{\"name\":\"tree-2a\",\"value\":61,\"regional\":true,\"color_hint\":\"00A000\",\"description\":\"Tree
        cover, broadleaved, deciduous, closed (>40%)\"},{\"name\":\"tree-2b\",\"value\":62,\"regional\":true,\"color_hint\":\"AAC800\",\"description\":\"Tree
        cover, broadleaved, deciduous, open (15-40%)\"},{\"name\":\"tree-3\",\"value\":70,\"color_hint\":\"003C00\",\"description\":\"Tree
        cover, needleleaved, evergreen, closed to open (>15%)\"},{\"name\":\"tree-3a\",\"value\":71,\"regional\":true,\"color_hint\":\"003C00\",\"description\":\"Tree
        cover, needleleaved, evergreen, closed (>40%)\"},{\"name\":\"tree-3b\",\"value\":72,\"regional\":true,\"color_hint\":\"005000\",\"description\":\"Tree
        cover, needleleaved, evergreen, open (15-40%)\"},{\"name\":\"tree-4\",\"value\":80,\"color_hint\":\"285000\",\"description\":\"Tree
        cover, needleleaved, deciduous, closed to open (>15%)\"},{\"name\":\"tree-4a\",\"value\":81,\"regional\":true,\"color_hint\":\"285000\",\"description\":\"Tree
        cover, needleleaved, deciduous, closed (>40%)\"},{\"name\":\"tree-4b\",\"value\":82,\"regional\":true,\"color_hint\":\"286400\",\"description\":\"Tree
        cover, needleleaved, deciduous, open (15-40%)\"},{\"name\":\"tree-5\",\"value\":90,\"color_hint\":\"788200\",\"description\":\"Tree
        cover, mixed leaf type (broadleaved and needleleaved)\"},{\"name\":\"tree-shrub\",\"value\":100,\"color_hint\":\"8CA000\",\"description\":\"Mosaic
        tree and shrub (>50%) / herbaceous cover (<50%)\"},{\"name\":\"herbaceous\",\"value\":110,\"color_hint\":\"BE9600\",\"description\":\"Mosaic
        herbaceous cover (>50%) / tree and shrub (<50%)\"},{\"name\":\"shrubland\",\"value\":120,\"color_hint\":\"966400\",\"description\":\"Shrubland\"},{\"name\":\"shrubland-a\",\"value\":121,\"regional\":true,\"color_hint\":\"966400\",\"description\":\"Evergreen
        shrubland\"},{\"name\":\"shrubland-b\",\"value\":122,\"regional\":true,\"color_hint\":\"966400\",\"description\":\"Deciduous
        shrubland\"},{\"name\":\"grassland\",\"value\":130,\"color_hint\":\"FFB432\",\"description\":\"Grassland\"},{\"name\":\"lichens-moses\",\"value\":140,\"color_hint\":\"FFDCD2\",\"description\":\"Lichens
        and mosses\"},{\"name\":\"sparse-veg\",\"value\":150,\"color_hint\":\"FFEBAF\",\"description\":\"Sparse
        vegetation (tree, shrub, herbaceous cover) (<15%)\"},{\"name\":\"sparse-veg-a\",\"value\":151,\"regional\":true,\"color_hint\":\"FFC864\",\"description\":\"Sparse
        tree (<15%)\"},{\"name\":\"sparse-veg-b\",\"value\":152,\"regional\":true,\"color_hint\":\"FFD278\",\"description\":\"Sparse
        shrub (<15%)\"},{\"name\":\"sparse-veg-c\",\"value\":153,\"regional\":true,\"color_hint\":\"FFEBAF\",\"description\":\"Sparse
        herbaceous cover (<15%)\"},{\"name\":\"flooded-tree-1\",\"value\":160,\"color_hint\":\"00785A\",\"description\":\"Tree
        cover, flooded, fresh or brackish water\"},{\"name\":\"flooded-tree-2\",\"value\":170,\"color_hint\":\"009678\",\"description\":\"Tree
        cover, flooded, saline water\"},{\"name\":\"flooded-shrub-herbaceous\",\"value\":180,\"color_hint\":\"00DC82\",\"description\":\"Shrub
        or herbaceous cover, flooded, fresh/saline/brackish water\"},{\"name\":\"urban\",\"value\":190,\"color_hint\":\"C31400\",\"description\":\"Urban
        areas\"},{\"name\":\"bare\",\"value\":200,\"color_hint\":\"FFF5D7\",\"description\":\"Bare
        areas\"},{\"name\":\"bare-a\",\"value\":201,\"regional\":true,\"color_hint\":\"DCDCDC\",\"description\":\"Consolidated
        bare areas\"},{\"name\":\"bare-b\",\"value\":202,\"regional\":true,\"color_hint\":\"FFF5D7\",\"description\":\"Unconsolidated
        bare areas\"},{\"name\":\"water\",\"value\":210,\"color_hint\":\"0046C8\",\"description\":\"Water
        bodies\"},{\"name\":\"snow-ice\",\"value\":220,\"color_hint\":\"FFFFFF\",\"description\":\"Permanent
        snow and ice\"}]},\"change_count\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"quality\"],\"title\":\"Number of Class
        Changes\",\"description\":\"Number of years where land cover class changes
        have occurred, since 1992. 0 for stable, greater than 0 for changes.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":300}]},\"processed_flag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"quality\"],\"title\":\"Land
        Cover Map Processed Area Flag\",\"description\":\"Flag to mark areas that
        could not be classified.\",\"raster:bands\":[{\"nodata\":255,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":300}],\"classification:classes\":[{\"name\":\"not_processed\",\"value\":0,\"description\":\"Not
        processed\"},{\"name\":\"processed\",\"value\":1,\"description\":\"Processed\"}]},\"observation_count\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"quality\"],\"title\":\"Number
        of Valid Observations\",\"description\":\"Number of valid satellite observations
        that have contributed to each pixel's classification.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":300}]},\"current_pixel_state\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"quality\"],\"title\":\"Land
        Cover Pixel Type Mask\",\"description\":\"Pixel identification from satellite
        surface reflectance observations, mainly distinguishing between land, water,
        and snow/ice.\",\"raster:bands\":[{\"nodata\":255,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":300}],\"classification:classes\":[{\"name\":\"land\",\"value\":1,\"description\":\"Clear
        land\"},{\"name\":\"water\",\"value\":2,\"description\":\"Clear water\"},{\"name\":\"snow\",\"value\":3,\"description\":\"Clear
        snow / ice\"},{\"name\":\"cloud\",\"value\":4,\"description\":\"Cloud\"},{\"name\":\"cloud_shadow\",\"value\":5,\"description\":\"Cloud
        shadow\"},{\"name\":\"filled\",\"value\":6,\"description\":\"Filled\"}]}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"esa-cci-lc\",\"msft:container\":\"esa-cci-lc\",\"stac_extensions\":[\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\",\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"Tiled
        ESA CCI global land cover maps in COG format\",\"msft:region\":\"westeurope\"},{\"id\":\"esa-cci-lc-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc-netcdf\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/satellite-land-cover.pdf\",\"type\":\"text/html\",\"title\":\"ESA
        CCI license\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\",\"type\":\"text/html\",\"title\":\"COPERNICUS
        license\"},{\"rel\":\"license\",\"href\":\"https://cds.climate.copernicus.eu/api/v2/terms/static/vito-proba-v.pdf\",\"type\":\"text/html\",\"title\":\"VITO
        License\"},{\"rel\":\"about\",\"href\":\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover\",\"type\":\"text/html\",\"title\":\"Product
        Landing Page\"},{\"rel\":\"about\",\"href\":\"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        user guide for version 2.1\"},{\"rel\":\"about\",\"href\":\"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        user guide for version 2.0\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.24381/cds.006f2c9a\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ESA
        Climate Change Initiative Land Cover Maps (NetCDF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/esa-cci-lc-netcdf-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"ESA
        CCI Land Cover NetCDF Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/esa-cci-lc-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-90.0,180.0,90.0]]},\"temporal\":{\"interval\":[[\"1992-01-01T00:00:00Z\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.24381/cds.006f2c9a\",\"keywords\":[\"Land
        Cover\",\"ESA\",\"CCI\",\"Global\"],\"providers\":[{\"url\":\"https://vito.be\",\"name\":\"VITO\",\"roles\":[\"licensor\"],\"description\":\"Provides
        the PROBA-V source data (for v2.0).\"},{\"url\":\"https://uclouvain.be\",\"name\":\"UCLouvain\",\"roles\":[\"producer\"],\"description\":\"UCLouvain
        produces the dataset (v2.1) for the ESA Climate Change Initiative.\"},{\"url\":\"https://brockmann-consult.de\",\"name\":\"Brockmann
        Consult\",\"roles\":[\"processor\"],\"description\":\"Brockmann Consult is
        responsible for the required pre-processing and the distribution of the dataset
        (v2.1).\"},{\"url\":\"http://esa-landcover-cci.org\",\"name\":\"ESA Climate
        Change Initiative\",\"roles\":[\"licensor\"],\"description\":\"The ESA Climate
        Change Initiative (CCI) is leading the product creation.\"},{\"url\":\"https://copernicus.eu\",\"name\":\"Copernicus\",\"roles\":[\"licensor\"],\"description\":\"Hosts
        the data on the Copernicus Climate Data Store (CDS).\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"esa_cci_lc:version\":[\"2.0.7cds\",\"2.1.1\"]},\"description\":\"The
        ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview)
        provides consistent global annual land cover maps at 300m spatial resolution
        from 1992 to 2020. The land cover classes are defined using the United Nations
        Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/)
        (LCCS). In addition to the land cover maps, four quality flags are produced
        to document the reliability of the classification and change detection. \\n\\nThe
        data in this Collection are the original NetCDF files accessed from the [Copernicus
        Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend
        users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc),
        which provides the data as Cloud Optimized GeoTIFFs.\",\"item_assets\":{\"netcdf\":{\"type\":\"application/netcdf\",\"roles\":[\"data\",\"quality\"],\"title\":\"ESA
        CCI Land Cover NetCDF 4 File\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"esa-cci-lc\",\"msft:container\":\"esa-cci-lc\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"ESA
        CCI global land cover maps in NetCDF format\",\"msft:region\":\"westeurope\"},{\"id\":\"fws-nwi\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi\"},{\"rel\":\"describedby\",\"href\":\"https://www.fws.gov/wetlands/Data/metadata/FWS_Wetlands.xml\",\"type\":\"application/xml\",\"title\":\"Wetlands
        metadata\"},{\"rel\":\"about\",\"href\":\"https://www.fws.gov/sites/default/files/documents/national-wetlands-inventory-fact-sheet.pdf\",\"type\":\"application/pdf\",\"title\":\"Project
        Fact Sheet\"},{\"rel\":\"about\",\"href\":\"https://www.fws.gov/program/national-wetlands-inventory\",\"type\":\"text/html\",\"title\":\"Project
        Landing Page\"},{\"rel\":\"license\",\"href\":\"http://www.usa.gov/publicdomain/label/1.0/\",\"type\":\"text/html\",\"title\":\"US
        Public Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/fws-nwi\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"FWS
        National Wetlands Inventory\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fws-nwi-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"FWS
        National Wetlands Inventory thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-64.54958,13.16667,144.6,71.99633],[144.6,13.16667,180.0,71.99633],[-180.0,13.16667,-64.54958,71.99633]]},\"temporal\":{\"interval\":[[\"2022-10-01T00:00:00Z\",\"2022-10-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"USFWS\",\"Wetlands\",\"United
        States\"],\"providers\":[{\"url\":\"https://www.fws.gov\",\"name\":\"U.S.
        Fish and Wildlife Service\",\"email\":\"wetlands_team@fws.gov\",\"roles\":[\"producer\",\"licensor\"],\"description\":\"The
        U.S. Fish and Wildlife Service is the principal federal agency tasked with
        providing information to the public on the extent and status of the nation's
        wetland and deepwater habitats, as well as changes to these habitats over
        time.\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"fws_nwi:state\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District
        of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New
        Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North
        Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pacific Trust Islands\",\"Pennsylvania\",\"Puerto
        Rico and Virgin Islands\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West
        Virginia\",\"Wisconsin\",\"Wyoming\"],\"fws_nwi:content\":[\"riparian\",\"historic_wetlands\",\"wetlands\"],\"fws_nwi:state_code\":[\"AL\",\"AK\",\"AZ\",\"AR\",\"CA\",\"CO\",\"CT\",\"DE\",\"DC\",\"FL\",\"GA\",\"HI\",\"ID\",\"IL\",\"IN\",\"IA\",\"KS\",\"KY\",\"LA\",\"ME\",\"MD\",\"MA\",\"MI\",\"MN\",\"MS\",\"MO\",\"MT\",\"NE\",\"NV\",\"NH\",\"NJ\",\"NM\",\"NY\",\"NC\",\"ND\",\"OH\",\"OK\",\"OR\",\"PacTrust\",\"PA\",\"PRVI\",\"RI\",\"SC\",\"SD\",\"TN\",\"TX\",\"UT\",\"VT\",\"VA\",\"WA\",\"WV\",\"WI\",\"WY\"]},\"description\":\"The
        Wetlands Data Layer is the product of over 45 years of work by the National
        Wetlands Inventory (NWI) and its collaborators and currently contains more
        than 35 million wetland and deepwater features. This dataset, covering the
        conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam,
        the major Northern Mariana Islands and Alaska, continues to grow at a rate
        of 50 to 100 million acres annually as data are updated.\\n\\n**NOTE:** Due
        to the variation in use and analysis of this data by the end user, each  state's
        wetlands data extends beyond the state boundary. Each state includes wetlands
        data that intersect the 1:24,000 quadrangles that contain part of that state
        (1:2,000,000 source data). This allows the user to clip the data to their
        specific analysis datasets. Beware that two adjacent states will contain some
        of the same data along their borders.\\n\\nFor more information, visit the
        National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\\n\\n##
        STAC Metadata\\n\\nIn addition to the `zip` asset in every STAC item, each
        item has its own assets unique to its wetlands. In general, each item will
        have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet)
        asset with data for the entire region or a sub-region within that state. Use
        the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles)
        to select just the geoparquet assets. See the Example Notebook for more.\",\"item_assets\":{\"zip\":{\"type\":\"application/zip\",\"roles\":[\"data\",\"archive\",\"source\"]}},\"stac_version\":\"1.0.0\",\"msft:container\":\"fws-nwi\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Vector
        dataset containing wetlands boundaries and identification across the United
        States.\",\"msft:region\":\"westeurope\"},{\"id\":\"usgs-lcmap-conus-v13\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13\"},{\"rel\":\"about\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products\",\"type\":\"text/html\",\"title\":\"LCMAP
        CONUS Science Products\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products\",\"type\":\"text/html\",\"title\":\"Proprietary,
        Unrestricted\"},{\"rel\":\"source\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\",\"type\":\"text/html\",\"title\":\"USGS
        Data Access Options\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.1016/j.rse.2019.111356\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.1016/j.rse.2014.01.011\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5066/P9C46NG0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/usgs-lcmap-conus-v13\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        LCMAP CONUS Collection 1.3\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usgs-lcmap-conus-v13-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"USGS
        LCMAP CONUS 1.3 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/usgs-lcmap-conus-v13.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-129.27732,21.805095,-63.11843,52.92172]]},\"temporal\":{\"interval\":[[\"1985-01-01T00:00:00Z\",\"2021-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.5066/P9C46NG0\",\"keywords\":[\"USGS\",\"LCMAP\",\"Land
        Cover\",\"Land Cover Change\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.usgs.gov/special-topics/lcmap\",\"name\":\"United
        States Geological Survey\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"usgs_lcmap:vertical_tile\":{\"maximum\":20,\"minimum\":0},\"usgs_lcmap:horizontal_tile\":{\"maximum\":32,\"minimum\":1}},\"description\":\"The
        [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap)
        (LCMAP) product provides land cover mapping and change monitoring from the
        U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros)
        (EROS) Center. LCMAP's Science Products are developed by applying time-series
        modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data)
        (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011)
        (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations
        are fit to a harmonic model to predict future Landsat-like surface reflectance.
        Where Landsat surface reflectance observations differ significantly from those
        predictions, a change is identified. Attributes of the resulting model sequences
        (e.g., start/end dates, residuals, model coefficients) are then used to produce
        a set of land surface change products and as inputs to the subsequent classification
        to thematic land cover. \\n\\nThis [STAC](https://stacspec.org/en) Collection
        contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products),
        which was released in August 2022 for years 1985-2021. The data are tiled
        according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/)
        (COGs) and corresponding metadata files. Note that the provided COGs differ
        slightly from those in the USGS source data. They have been reprocessed to
        add overviews, \\\"nodata\\\" values where appropriate, and an updated projection
        definition.\\n\",\"item_assets\":{\"dates\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Landsat
        Observation Dates\",\"description\":\"Landsat observation dates used as input
        to the CCDC algorithm.\"},\"lcpri\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover\",\"description\":\"Land
        cover classification consisting of eight general land cover types. The most
        likely land cover according to the modeling process.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"}]},\"lcsec\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary
        Land Cover\",\"description\":\"Land cover classification consisting of eight
        general land cover types. The second most likely land cover according to the
        modeling process.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"}]},\"scmag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Change
        Magnitude\",\"description\":\"The spectral strength or intensity of a time
        series model 'break' when spectral observations have diverged from the model
        predictions.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"float32\",\"spatial_resolution\":30}]},\"scmqa\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Model
        Quality\",\"description\":\"Information regarding the type of time series
        model applied to the current product year.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"no_model\",\"value\":0,\"description\":\"No
        model established for July 1st of current year.\"},{\"name\":\"simple_model\",\"value\":4,\"description\":\"A
        partial, 4-coefficient harmonic model.\"},{\"name\":\"advanced_model\",\"value\":6,\"description\":\"A
        partial, 6-coefficient harmonic model.\"},{\"name\":\"full_model\",\"value\":8,\"description\":\"A
        full, 8-coefficient harmonic model.\"},{\"name\":\"start_fit\",\"value\":14,\"description\":\"A
        simple model at the beginning of a time series where sparse and/or highly
        variable spectral measurements prevent establishment of a harmonic model.\"},{\"name\":\"end_fit\",\"value\":24,\"description\":\"A
        simple model at the end of a time series where there are insufficient observations
        and/or time to establish a new harmonic model following a model break.\"},{\"name\":\"insufficient_clear\",\"value\":44,\"description\":\"A
        simple model for the entire time series in cases where fewer than 25% of input
        observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel
        quality band (PIXELQA).\"},{\"name\":\"persistent_snow\",\"value\":54,\"description\":\"A
        simple model for the entire time series in cases where 75% or more of input
        observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality
        band (PIXELQA).\"}]},\"browse\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"USGS Browse Image\",\"description\":\"Image
        generated by USGS for viewing LCMAP classification data in web applications.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcachg\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        Land Cover Change\",\"description\":\"Synthesis of Primary Land Cover of current
        and previous year identifying changes in land cover class.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"},{\"name\":\"1_to_2\",\"value\":12,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Cropland\"},{\"name\":\"1_to_3\",\"value\":13,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Grass/Shrub\"},{\"name\":\"1_to_4\",\"value\":14,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Tree Cover\"},{\"name\":\"1_to_5\",\"value\":15,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Water\"},{\"name\":\"1_to_6\",\"value\":16,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Wetlands\"},{\"name\":\"1_to_7\",\"value\":17,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Snow/Ice\"},{\"name\":\"1_to_8\",\"value\":18,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Barren\"},{\"name\":\"2_to_1\",\"value\":21,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Developed\"},{\"name\":\"2_to_3\",\"value\":23,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Grass/Shrub\"},{\"name\":\"2_to_4\",\"value\":24,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Tree Cover\"},{\"name\":\"2_to_5\",\"value\":25,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Water\"},{\"name\":\"2_to_6\",\"value\":26,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Wetlands\"},{\"name\":\"2_to_7\",\"value\":27,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Snow/Ice\"},{\"name\":\"2_to_8\",\"value\":28,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Barren\"},{\"name\":\"3_to_1\",\"value\":31,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Developed\"},{\"name\":\"3_to_2\",\"value\":32,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Cropland\"},{\"name\":\"3_to_4\",\"value\":34,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Tree Cover\"},{\"name\":\"3_to_5\",\"value\":35,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Water\"},{\"name\":\"3_to_6\",\"value\":36,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Wetlands\"},{\"name\":\"3_to_7\",\"value\":37,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Snow/Ice\"},{\"name\":\"3_to_8\",\"value\":38,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Barren\"},{\"name\":\"4_to_1\",\"value\":41,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Developed\"},{\"name\":\"4_to_2\",\"value\":42,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Cropland\"},{\"name\":\"4_to_3\",\"value\":43,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Grass/Shrub\"},{\"name\":\"4_to_5\",\"value\":45,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Water\"},{\"name\":\"4_to_6\",\"value\":46,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Wetlands\"},{\"name\":\"4_to_7\",\"value\":47,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Snow/Ice\"},{\"name\":\"4_to_8\",\"value\":48,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Barren\"},{\"name\":\"5_to_1\",\"value\":51,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Developed\"},{\"name\":\"5_to_2\",\"value\":52,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Cropland\"},{\"name\":\"5_to_3\",\"value\":53,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Grass/Shrub\"},{\"name\":\"5_to_4\",\"value\":54,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Tree Cover\"},{\"name\":\"5_to_6\",\"value\":56,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Wetlands\"},{\"name\":\"5_to_7\",\"value\":57,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Snow/Ice\"},{\"name\":\"5_to_8\",\"value\":58,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Barren\"},{\"name\":\"6_to_1\",\"value\":61,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Developed\"},{\"name\":\"6_to_2\",\"value\":62,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Cropland\"},{\"name\":\"6_to_3\",\"value\":63,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Grass/Shrub\"},{\"name\":\"6_to_4\",\"value\":64,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Tree Cover\"},{\"name\":\"6_to_5\",\"value\":65,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Water\"},{\"name\":\"6_to_7\",\"value\":67,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Snow/Ice\"},{\"name\":\"6_to_8\",\"value\":68,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Barren\"},{\"name\":\"7_to_1\",\"value\":71,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Developed\"},{\"name\":\"7_to_2\",\"value\":72,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Cropland\"},{\"name\":\"7_to_3\",\"value\":73,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Grass/Shrub\"},{\"name\":\"7_to_4\",\"value\":74,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Tree Cover\"},{\"name\":\"7_to_5\",\"value\":75,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Water\"},{\"name\":\"7_to_6\",\"value\":76,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Wetlands\"},{\"name\":\"7_to_8\",\"value\":78,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Barren\"},{\"name\":\"8_to_1\",\"value\":81,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Developed\"},{\"name\":\"8_to_2\",\"value\":82,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Cropland\"},{\"name\":\"8_to_3\",\"value\":83,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Grass/Shrub\"},{\"name\":\"8_to_4\",\"value\":84,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Tree Cover\"},{\"name\":\"8_to_5\",\"value\":85,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Water\"},{\"name\":\"8_to_6\",\"value\":86,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Wetlands\"},{\"name\":\"8_to_7\",\"value\":87,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Snow/Ice\"}]},\"sclast\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time Since Last
        Change\",\"description\":\"Time, in days, since the last identified Spectral
        Change (SCTIME).\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"scstab\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Spectral
        Stability Period\",\"description\":\"Measure of the amount of time in days
        that a pixel has been in its current spectral state as of July 1st. Current
        spectral state can refer to both during stable time series segments or a period
        outside of stable time series segments.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"sctime\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time
        of Spectral Change\",\"description\":\"Represents the timing of a spectral
        change within the current product year as the day of year the change occurred.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"lcpconf\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary
        Land Cover Confidence\",\"description\":\"Provides provenance tracking and
        a measure of confidence that the Primary Land Cover label matches the training
        data.\",\"file:values\":[{\"values\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\"summary\":\"Measure
        of confidence that the Primary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time
        series model identified as transition from a Grass/Shrub class to a Tree Cover
        class. Primary Land Cover class assignment based on secondary analysis.\"},{\"values\":[152],\"summary\":\"Time
        series model identified as transition from a Tree Cover class to a Grass/Shrub
        class. Primary Land cover class assignment based on secondary analysis.\"},{\"values\":[201],\"summary\":\"No
        stable time series models were produced for this location. Primary Land Cover
        was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP
        Level 1 classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient
        data available to extend most recent time series model past July 1st of current
        year. Land cover assigned the last identified cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July
        1st falls in a gap between two stable time series models of the same land
        cover class. Primary Land Cover assigned the primary land cover class of those
        before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in
        a gap between two stable time series models of differing land cover class.
        If July 1st is before the 'break date' of the earlier model, Primary Land
        Cover is assigned the primary land cover class of that earlier model. Otherwise,
        Primary Land Cover is assigned the primary land cover class of the subsequent,
        later model.\"},{\"values\":[213],\"summary\":\"Insufficient data available
        to establish a stable time series model at the beginning of the time series
        prior to July 1st of the current year. Primary Land Cover assigned the primary
        land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient
        data available to establish a new stable time series model following a break
        near the end of the time series prior to July 1st of the current year. Primary
        Land Cover assigned the last identified primary land cover class from earlier
        year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcsconf\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary
        Land Cover Confidence\",\"description\":\"Provides provenance tracking and
        a measure of confidence that the Secondary Land Cover label matches the training
        data.\",\"file:values\":[{\"values\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\"summary\":\"Measure
        of confidence that the Secondary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time
        series model identified as transition from a Grass/Shrub class to a Tree Cover
        class in Primary Land Cover. Primary Land Cover class assignment based on
        secondary analysis and Secondary Land Cover class assigned logical opposite
        of Primary.\"},{\"values\":[152],\"summary\":\"Time series model identified
        as transition from a Tree Cover class to a Grass/Shrub class in Primary Land
        Cover. Primary Land Cover class assignment based on secondary analysis and
        Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[201],\"summary\":\"No
        stable time series models were produced for this location. Secondary Land
        Cover assigned the land cover class present in NLCD-2001 (cross-walked to
        LCMAP classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient
        data available to extend most recent time series model past July 1st of current
        year. Secondary Land Cover assigned the last identified secondary cover class
        from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a
        gap between two stable time series models of the same secondary land cover
        class. Secondary Land Cover assigned the land cover class of those before/after
        models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between
        two stable time series models of differing secondary land cover classes. If
        July 1st is before the 'break date' of the earlier model, Secondary Land Cover
        is assigned the secondary land cover class of the earlier model. Otherwise,
        Secondary Land Cover is assigned the secondary land cover class of the subsequent
        model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish
        a stable time series model at the beginning of the time series prior to July
        1st of the current year. Secondary Land Cover assigned the secondary land
        cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient
        data available to establish a new stable time series model following a break
        near the end of the time series prior to July 1st of the current year. Secondary
        Land Cover assigned the last identified secondary cover class from earlier
        year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcpri_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary
        Land Cover Metadata\",\"description\":\"Primary Land Cover product metadata\"},\"lcsec_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary
        Land Cover Metadata\",\"description\":\"Secondary Land Cover product metadata\"},\"scmag_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Change
        Magnitude Metadata\",\"description\":\"Change Magnitude product metadata\"},\"scmqa_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Model
        Quality Metadata\",\"description\":\"Model Quality product metadata\"},\"lcachg_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Annual
        Land Cover Change Metadata\",\"description\":\"Annual Land Cover Change product
        metadata\"},\"sclast_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time
        Since Last Change Metadata\",\"description\":\"Time Since Last Change product
        metadata\"},\"scstab_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Spectral
        Stability Period Metadata\",\"description\":\"Spectral Stability Period product
        metadata\"},\"sctime_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time
        of Spectral Change Metadata\",\"description\":\"Time of Spectral Change product
        metadata\"},\"lcpconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary
        Land Cover Confidence Metadata\",\"description\":\"Primary Land Cover Confidence
        product metadata\"},\"lcsconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary
        Land Cover Confidence Metadata\",\"description\":\"Secondary Land Cover Confidence
        product metadata\"}},\"sci:citation\":\"U.S. Geological Survey (USGS), 2022,
        Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3
        Science Products for the Conterminous United States: USGS data release\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"usgs-lcmap\",\"msft:container\":\"lcmap\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.1016/j.rse.2019.111356\",\"citation\":\"Brown,
        J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E.,
        Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith,
        K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant,
        A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing
        an operational continuous United States national land change monitoring capability-The
        Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote
        Sensing of Environment, v. 238, article 111356\"},{\"doi\":\"10.1016/j.rse.2014.01.011\",\"citation\":\"Zhu,
        Z., and Woodcock, C.E., 2014, Continuous change detection and classification
        of land cover using all available Landsat data: Remote Sensing of Environment,
        v. 144, p. 152-171\"}],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"USGS
        Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3
        Science Products for the Conterminous United States.\",\"msft:region\":\"westeurope\"},{\"id\":\"usgs-lcmap-hawaii-v10\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10\"},{\"rel\":\"about\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products\",\"type\":\"text/html\",\"title\":\"LCMAP
        Hawaii Science Products\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products\",\"type\":\"text/html\",\"title\":\"Proprietary,
        Unrestricted\"},{\"rel\":\"source\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\",\"type\":\"text/html\",\"title\":\"USGS
        Data Access Options\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.1016/j.jag.2022.103015\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.1016/j.rse.2019.111356\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5066/P91E8M23\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/usgs-lcmap-hawaii-v10\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS
        LCMAP Hawaii Collection 1.0\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usgs-lcmap-hawaii-v10-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"USGS
        LCMAP Hawaii 1.0 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/usgs-lcmap-hawaii-v10.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-161.27577,18.505136,-154.058649,22.624478]]},\"temporal\":{\"interval\":[[\"2000-01-01T00:00:00Z\",\"2020-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.5066/P91E8M23\",\"keywords\":[\"USGS\",\"LCMAP\",\"Land
        Cover\",\"Land Cover Change\",\"Hawaii\"],\"providers\":[{\"url\":\"https://www.usgs.gov/special-topics/lcmap\",\"name\":\"United
        States Geological Survey\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"usgs_lcmap:vertical_tile\":{\"maximum\":2,\"minimum\":0},\"usgs_lcmap:horizontal_tile\":{\"maximum\":4,\"minimum\":0}},\"description\":\"The
        [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap)
        (LCMAP) product provides land cover mapping and change monitoring from the
        U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros)
        (EROS) Center. LCMAP's Science Products are developed by applying time-series
        modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data)
        (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011)
        (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations
        are fit to a harmonic model to predict future Landsat-like surface reflectance.
        Where Landsat surface reflectance observations differ significantly from those
        predictions, a change is identified. Attributes of the resulting model sequences
        (e.g., start/end dates, residuals, model coefficients) are then used to produce
        a set of land surface change products and as inputs to the subsequent classification
        to thematic land cover. \\n\\nThis [STAC](https://stacspec.org/en) Collection
        contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products),
        which was released in January 2022 for years 2000-2020. The data are tiled
        according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/)
        (COGs) and corresponding metadata files. Note that the provided COGs differ
        slightly from those in the USGS source data. They have been reprocessed to
        add overviews, \\\"nodata\\\" values where appropriate, and an updated projection
        definition.\\n\",\"item_assets\":{\"dates\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Landsat
        Observation Dates\",\"description\":\"Landsat observation dates used as input
        to the CCDC algorithm.\"},\"lcpri\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover\",\"description\":\"Land
        cover classification consisting of eight general land cover types. The most
        likely land cover according to the modeling process.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"}]},\"lcsec\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary
        Land Cover\",\"description\":\"Land cover classification consisting of eight
        general land cover types. The second most likely land cover according to the
        modeling process.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"}]},\"scmag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Change
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        series model 'break' when spectral observations have diverged from the model
        predictions.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"float32\",\"spatial_resolution\":30}]},\"scmqa\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Model
        Quality\",\"description\":\"Information regarding the type of time series
        model applied to the current product year.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"no_model\",\"value\":0,\"description\":\"No
        model established for July 1st of current year.\"},{\"name\":\"simple_model\",\"value\":4,\"description\":\"A
        partial, 4-coefficient harmonic model.\"},{\"name\":\"advanced_model\",\"value\":6,\"description\":\"A
        partial, 6-coefficient harmonic model.\"},{\"name\":\"full_model\",\"value\":8,\"description\":\"A
        full, 8-coefficient harmonic model.\"},{\"name\":\"start_fit\",\"value\":14,\"description\":\"A
        simple model at the beginning of a time series where sparse and/or highly
        variable spectral measurements prevent establishment of a harmonic model.\"},{\"name\":\"end_fit\",\"value\":24,\"description\":\"A
        simple model at the end of a time series where there are insufficient observations
        and/or time to establish a new harmonic model following a model break.\"},{\"name\":\"insufficient_clear\",\"value\":44,\"description\":\"A
        simple model for the entire time series in cases where fewer than 25% of input
        observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel
        quality band (PIXELQA).\"},{\"name\":\"persistent_snow\",\"value\":54,\"description\":\"A
        simple model for the entire time series in cases where 75% or more of input
        observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality
        band (PIXELQA).\"}]},\"browse\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"USGS Browse Image\",\"description\":\"Image
        generated by USGS for viewing LCMAP classification data in web applications.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcachg\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual
        Land Cover Change\",\"description\":\"Synthesis of Primary Land Cover of current
        and previous year identifying changes in land cover class.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"name\":\"nodata\",\"value\":0,\"color_hint\":\"000000\",\"description\":\"No
        Data\"},{\"name\":\"developed\",\"value\":1,\"color_hint\":\"ff3232\",\"description\":\"Developed\"},{\"name\":\"cropland\",\"value\":2,\"color_hint\":\"be8c5a\",\"description\":\"Cropland\"},{\"name\":\"grass_shrub\",\"value\":3,\"color_hint\":\"e6f0d2\",\"description\":\"Grass/Shrub\"},{\"name\":\"tree_cover\",\"value\":4,\"color_hint\":\"1c6330\",\"description\":\"Tree
        Cover\"},{\"name\":\"water\",\"value\":5,\"color_hint\":\"0070ff\",\"description\":\"Water\"},{\"name\":\"wetlands\",\"value\":6,\"color_hint\":\"b3d9ff\",\"description\":\"Wetlands\"},{\"name\":\"ice_snow\",\"value\":7,\"color_hint\":\"ffffff\",\"description\":\"Snow/Ice\"},{\"name\":\"barren\",\"value\":8,\"color_hint\":\"b3aea3\",\"description\":\"Barren\"},{\"name\":\"1_to_2\",\"value\":12,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Cropland\"},{\"name\":\"1_to_3\",\"value\":13,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Grass/Shrub\"},{\"name\":\"1_to_4\",\"value\":14,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Tree Cover\"},{\"name\":\"1_to_5\",\"value\":15,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Water\"},{\"name\":\"1_to_6\",\"value\":16,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Wetlands\"},{\"name\":\"1_to_7\",\"value\":17,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Snow/Ice\"},{\"name\":\"1_to_8\",\"value\":18,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Developed to Barren\"},{\"name\":\"2_to_1\",\"value\":21,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Developed\"},{\"name\":\"2_to_3\",\"value\":23,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Grass/Shrub\"},{\"name\":\"2_to_4\",\"value\":24,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Tree Cover\"},{\"name\":\"2_to_5\",\"value\":25,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Water\"},{\"name\":\"2_to_6\",\"value\":26,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Wetlands\"},{\"name\":\"2_to_7\",\"value\":27,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Snow/Ice\"},{\"name\":\"2_to_8\",\"value\":28,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Cropland to Barren\"},{\"name\":\"3_to_1\",\"value\":31,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Developed\"},{\"name\":\"3_to_2\",\"value\":32,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Cropland\"},{\"name\":\"3_to_4\",\"value\":34,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Tree Cover\"},{\"name\":\"3_to_5\",\"value\":35,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Water\"},{\"name\":\"3_to_6\",\"value\":36,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Wetlands\"},{\"name\":\"3_to_7\",\"value\":37,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Snow/Ice\"},{\"name\":\"3_to_8\",\"value\":38,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Grass/Shrub to Barren\"},{\"name\":\"4_to_1\",\"value\":41,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Developed\"},{\"name\":\"4_to_2\",\"value\":42,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Cropland\"},{\"name\":\"4_to_3\",\"value\":43,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Grass/Shrub\"},{\"name\":\"4_to_5\",\"value\":45,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Water\"},{\"name\":\"4_to_6\",\"value\":46,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Wetlands\"},{\"name\":\"4_to_7\",\"value\":47,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Snow/Ice\"},{\"name\":\"4_to_8\",\"value\":48,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Tree Cover to Barren\"},{\"name\":\"5_to_1\",\"value\":51,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Developed\"},{\"name\":\"5_to_2\",\"value\":52,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Cropland\"},{\"name\":\"5_to_3\",\"value\":53,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Grass/Shrub\"},{\"name\":\"5_to_4\",\"value\":54,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Tree Cover\"},{\"name\":\"5_to_6\",\"value\":56,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Wetlands\"},{\"name\":\"5_to_7\",\"value\":57,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Snow/Ice\"},{\"name\":\"5_to_8\",\"value\":58,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Water to Barren\"},{\"name\":\"6_to_1\",\"value\":61,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Developed\"},{\"name\":\"6_to_2\",\"value\":62,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Cropland\"},{\"name\":\"6_to_3\",\"value\":63,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Grass/Shrub\"},{\"name\":\"6_to_4\",\"value\":64,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Tree Cover\"},{\"name\":\"6_to_5\",\"value\":65,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Water\"},{\"name\":\"6_to_7\",\"value\":67,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Snow/Ice\"},{\"name\":\"6_to_8\",\"value\":68,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Wetlands to Barren\"},{\"name\":\"7_to_1\",\"value\":71,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Developed\"},{\"name\":\"7_to_2\",\"value\":72,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Cropland\"},{\"name\":\"7_to_3\",\"value\":73,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Grass/Shrub\"},{\"name\":\"7_to_4\",\"value\":74,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Tree Cover\"},{\"name\":\"7_to_5\",\"value\":75,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Water\"},{\"name\":\"7_to_6\",\"value\":76,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Wetlands\"},{\"name\":\"7_to_8\",\"value\":78,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Snow/Ice to Barren\"},{\"name\":\"8_to_1\",\"value\":81,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Developed\"},{\"name\":\"8_to_2\",\"value\":82,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Cropland\"},{\"name\":\"8_to_3\",\"value\":83,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Grass/Shrub\"},{\"name\":\"8_to_4\",\"value\":84,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Tree Cover\"},{\"name\":\"8_to_5\",\"value\":85,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Water\"},{\"name\":\"8_to_6\",\"value\":86,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Wetlands\"},{\"name\":\"8_to_7\",\"value\":87,\"color_hint\":\"ab00d6\",\"description\":\"Cover
        change from Barren to Snow/Ice\"}]},\"sclast\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time Since Last
        Change\",\"description\":\"Time, in days, since the last identified Spectral
        Change (SCTIME).\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"scstab\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Spectral
        Stability Period\",\"description\":\"Measure of the amount of time in days
        that a pixel has been in its current spectral state as of July 1st. Current
        spectral state can refer to both during stable time series segments or a period
        outside of stable time series segments.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"sctime\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time
        of Spectral Change\",\"description\":\"Represents the timing of a spectral
        change within the current product year as the day of year the change occurred.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"lcpconf\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary
        Land Cover Confidence\",\"description\":\"Provides provenance tracking and
        a measure of confidence that the Primary Land Cover label matches the training
        data.\",\"file:values\":[{\"values\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\"summary\":\"Measure
        of confidence that the Primary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time
        series model identified as transition from a Grass/Shrub class to a Tree Cover
        class. Primary Land Cover class assignment based on secondary analysis.\"},{\"values\":[152],\"summary\":\"Time
        series model identified as transition from a Tree Cover class to a Grass/Shrub
        class. Primary Land cover class assignment based on secondary analysis.\"},{\"values\":[201],\"summary\":\"No
        stable time series models were produced for this location. Primary Land Cover
        was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP
        Level 1 classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient
        data available to extend most recent time series model past July 1st of current
        year. Land cover assigned the last identified cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July
        1st falls in a gap between two stable time series models of the same land
        cover class. Primary Land Cover assigned the primary land cover class of those
        before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in
        a gap between two stable time series models of differing land cover class.
        If July 1st is before the 'break date' of the earlier model, Primary Land
        Cover is assigned the primary land cover class of that earlier model. Otherwise,
        Primary Land Cover is assigned the primary land cover class of the subsequent,
        later model.\"},{\"values\":[213],\"summary\":\"Insufficient data available
        to establish a stable time series model at the beginning of the time series
        prior to July 1st of the current year. Primary Land Cover assigned the primary
        land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient
        data available to establish a new stable time series model following a break
        near the end of the time series prior to July 1st of the current year. Primary
        Land Cover assigned the last identified primary land cover class from earlier
        year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcsconf\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary
        Land Cover Confidence\",\"description\":\"Provides provenance tracking and
        a measure of confidence that the Secondary Land Cover label matches the training
        data.\",\"file:values\":[{\"values\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\"summary\":\"Measure
        of confidence that the Secondary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time
        series model identified as transition from a Grass/Shrub class to a Tree Cover
        class in Primary Land Cover. Primary Land Cover class assignment based on
        secondary analysis and Secondary Land Cover class assigned logical opposite
        of Primary.\"},{\"values\":[152],\"summary\":\"Time series model identified
        as transition from a Tree Cover class to a Grass/Shrub class in Primary Land
        Cover. Primary Land Cover class assignment based on secondary analysis and
        Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[201],\"summary\":\"No
        stable time series models were produced for this location. Secondary Land
        Cover assigned the land cover class present in NLCD-2001 (cross-walked to
        LCMAP classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient
        data available to extend most recent time series model past July 1st of current
        year. Secondary Land Cover assigned the last identified secondary cover class
        from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a
        gap between two stable time series models of the same secondary land cover
        class. Secondary Land Cover assigned the land cover class of those before/after
        models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between
        two stable time series models of differing secondary land cover classes. If
        July 1st is before the 'break date' of the earlier model, Secondary Land Cover
        is assigned the secondary land cover class of the earlier model. Otherwise,
        Secondary Land Cover is assigned the secondary land cover class of the subsequent
        model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish
        a stable time series model at the beginning of the time series prior to July
        1st of the current year. Secondary Land Cover assigned the secondary land
        cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient
        data available to establish a new stable time series model following a break
        near the end of the time series prior to July 1st of the current year. Secondary
        Land Cover assigned the last identified secondary cover class from earlier
        year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcpri_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary
        Land Cover Metadata\",\"description\":\"Primary Land Cover product metadata\"},\"lcsec_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary
        Land Cover Metadata\",\"description\":\"Secondary Land Cover product metadata\"},\"scmag_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Change
        Magnitude Metadata\",\"description\":\"Change Magnitude product metadata\"},\"scmqa_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Model
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        Land Cover Change Metadata\",\"description\":\"Annual Land Cover Change product
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        metadata\"},\"lcpconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary
        Land Cover Confidence Metadata\",\"description\":\"Primary Land Cover Confidence
        product metadata\"},\"lcsconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary
        Land Cover Confidence Metadata\",\"description\":\"Secondary Land Cover Confidence
        product metadata\"}},\"sci:citation\":\"U.S. Geological Survey (USGS), 2022,
        Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0
        Science Products for Hawaii: USGS data release\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"usgs-lcmap\",\"msft:container\":\"lcmap\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.1016/j.jag.2022.103015\",\"citation\":\"Li,
        C., Xian, G., Wellington, D., Smith, K., Horton, J., &amp; Zhou, Q., 2022,
        Development of the LCMAP annual land cover product across Hawaii: International
        Journal of Applied Earth Observation and Geoinformation, v. 113, article 103015.\"},{\"doi\":\"10.1016/j.rse.2019.111356\",\"citation\":\"Brown,
        J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E.,
        Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith,
        K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant,
        A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing
        an operational continuous United States national land change monitoring capability-The
        Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote
        Sensing of Environment, v. 238, article 111356\"}],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"USGS
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        compare current weather and as a predictor of conditions in the near future.
        The official normals are calculated for a uniform 30 year period, and consist
        of annual/seasonal, monthly, daily, and hourly averages and statistics of
        temperature, precipitation, and other climatological variables for each weather
        station. \\n\\nNOAA produces Climate Normals in accordance with the [World
        Meteorological Organization](https://public.wmo.int/en) (WMO), of which the
        United States is a member. The WMO requires each member nation to compute
        30-year meteorological quantity averages at least every 30 years, and recommends
        an update each decade, in part to incorporate newer weather stations. The
        1991\u20132020 U.S. Climate Normals are the latest in a series of decadal
        normals first produced in the 1950s. \\n\\nThis Collection contains tabular
        weather variable data at weather station locations in GeoParquet format, converted
        from the source CSV files. The source NetCDF files are delivered to Azure
        as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\nData
        are provided for annual/seasonal, monthly, daily, and hourly frequencies for
        the following time periods:\\n\\n- Legacy 30-year normals (1981\u20132010)\\n-
        Supplemental 15-year normals (2006\u20132020)\\n\",\"item_assets\":{\"geoparquet\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset
        root\",\"table:storage_options\":{\"account_name\":\"noaanormals\"}}},\"msft:region\":\"eastus\",\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"1981_2010-hourly\",\"description\":\"Hourly
        Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-daily\",\"description\":\"Daily
        Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-monthly\",\"description\":\"Monthly
        Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-annualseasonal\",\"description\":\"Annual/Seasonal
        Climate Normals for Period 1981-2010\"},{\"name\":\"1991_2020-hourly\",\"description\":\"Hourly
        Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-daily\",\"description\":\"Daily
        Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-monthly\",\"description\":\"Monthly
        Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-annualseasonal\",\"description\":\"Annual/Seasonal
        Climate Normals for Period 1991-2020\"},{\"name\":\"2006_2020-hourly\",\"description\":\"Hourly
        Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-daily\",\"description\":\"Daily
        Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-monthly\",\"description\":\"Monthly
        Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-annualseasonal\",\"description\":\"Annual/Seasonal
        Climate Normals for Period 2006-2020\"}],\"msft:group_id\":\"noaa-climate-normals\",\"msft:container\":\"climate-normals-geoparquet\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"sci:publications\":[{\"doi\":\"10.1175/BAMS-D-11-00197.1\",\"citation\":\"Arguez,
        A., I. Durre, S. Applequist, R. Vose, M. Squires, X. Yin, R. Heim, and T.
        Owen, 2012: NOAA's 1981-2010 climate normals: An overview. Bull. Amer. Meteor.
        Soc., 93, 1687-1697. DOI: 10.1175/BAMS-D-11-00197.1.\"},{\"doi\":\"10.1175/BAMS-D-11-00173.1\",\"citation\":\"Applequist,
        S., A. Arguez, I. Durre, M. Squires, R. Vose, and X. Yin, 2012: 1981-2010
        U.S. Hourly Normals. Bulletin of the American Meteorological Society, 93,
        1637-1640. DOI: 10.1175/BAMS-D-11-00173.1.\"}],\"msft:storage_account\":\"noaanormals\",\"msft:short_description\":\"Tabular
        Climate Normal data for over 15,000 United States weather station locations
        in GeoParquet format.\"},{\"id\":\"noaa-climate-normals-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals\",\"type\":\"text/html\",\"title\":\"NOAA
        U.S. Climate Normals Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.noaa.gov/information-technology/open-data-dissemination\",\"type\":\"text/html\",\"title\":\"NOAA
        Open Data Dissemination\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf\",\"type\":\"application/pdf\",\"title\":\"Readme
        for Monthly Gridded Normals\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf\",\"type\":\"application/pdf\",\"title\":\"Documentation
        for Monthly Gridded Normals\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA
        US Gridded Climate Normals (NetCDF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-netcdf-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NOAA
        U.S. Gridded Climate Normals (NetCDF) Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-climate-normals-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.708333,24.541666,-66.999995,49.375001]]},\"temporal\":{\"interval\":[[\"1901-01-01T00:00:00Z\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"NOAA\",\"Climate
        Normals\",\"Weather\",\"Surface Observations\",\"Climatology\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA
        National Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"noaa_climate_normals:period\":[\"1901-2000\",\"1991-2020\",\"2006-2020\"],\"noaa_climate_normals:frequency\":[\"daily\",\"monthly\"]},\"description\":\"The
        [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027)
        provide a continuous grid of temperature and precipitation data across the
        contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid
        dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid),
        and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore
        match that of NClimGrid. Monthly, seasonal, and annual gridded normals are
        computed from simple averages of the NClimGrid data and are provided for three
        time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded
        normals are smoothed for a smooth transition from one day to another and are
        provided for two time-periods: 1991\u20132020, and 2006\u20132020.\\n\\nNOAA
        produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en)
        (WMO), of which the United States is a member. The WMO requires each member
        nation to compute 30-year meteorological quantity averages at least every
        30 years, and recommends an update each decade, in part to incorporate newer
        weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in
        a series of decadal normals first produced in the 1950s. \\n\\nThe data in
        this Collection are the original NetCDF files provided by NOAA's National
        Centers for Environmental Information. This Collection contains gridded data
        for the following frequencies and time periods:\\n\\n- Annual, seasonal, and
        monthly normals\\n    - 100-year (1901\u20132000)\\n    - 30-year (1991\u20132020)\\n
        \   - 15-year (2006\u20132020)\\n- Daily normals\\n    - 30-year (1991\u20132020)\\n
        \   - 15-year (2006\u20132020)\\n\\nFor most use-cases, we recommend using
        the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded)
        collection, which contains the same data in Cloud Optimized GeoTIFF format.
        The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination
        (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\",\"item_assets\":{\"netcdf\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"]}},\"msft:region\":\"eastus\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-climate-normals\",\"msft:container\":\"gridded-normals\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"noaanormals\",\"msft:short_description\":\"Gridded
        Climate Normals for the contiguous United States in the original NetCDF format
        provided by NOAA's National Centers for Environmental Information.\"},{\"id\":\"goes-glm\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm\"},{\"rel\":\"license\",\"href\":\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527\",\"title\":\"License\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527\",\"type\":\"text/html\",\"title\":\"Product
        Landing Page\"},{\"rel\":\"about\",\"href\":\"https://www.goes-r.gov/users/docs/PUG-main-vol1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        Definition and Users' Guide (PUG) Vol.1 Main\"},{\"rel\":\"about\",\"href\":\"https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf\",\"type\":\"application/pdf\",\"title\":\"Product
        Definition and Users' Guide (PUG) Vol.5 Level 2+ Products\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.7289/V5KH0KK6\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/goes-glm\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GOES-R
        Lightning Detection\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-glm-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"GOES
        GLM Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/goes-glm.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-66.56,-8.44,66.56],[156.44,-66.56,180.0,66.56]]},\"temporal\":{\"interval\":[[\"2018-02-13T16:10:00Z\",null]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.7289/V5KH0KK6\",\"keywords\":[\"GOES\",\"NOAA\",\"NASA\",\"Satellite\",\"Lightning\",\"Weather\"],\"providers\":[{\"url\":\"https://www.nasa.gov/content/goes\",\"name\":\"NASA\",\"roles\":[\"producer\"]},{\"url\":\"https://www.goes-r.gov/\",\"name\":\"NOAA\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[8000],\"mission\":[\"GOES\"],\"platform\":[\"GOES-16\",\"GOES-17\",\"GOES-18\",\"GOES-19\"],\"instruments\":[\"FM1\",\"FM2\"],\"constellation\":[\"GOES\"],\"processing:level\":[\"L2\"],\"goes:orbital-slot\":[\"West\",\"East\",\"Test\"]},\"description\":\"The
        [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html)
        is a single-channel, near-infrared optical transient detector that can detect
        the momentary changes in an optical scene, indicating the presence of lightning.
        GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground)
        activity continuously over the Americas and adjacent ocean regions with near-uniform
        spatial resolution of approximately 10 km. GLM collects information such as
        the frequency, location and extent of lightning discharges to identify intensifying
        thunderstorms and tropical cyclones. Trends in total lightning available from
        the GLM provide critical information to forecasters, allowing them to focus
        on developing severe storms much earlier and before these storms produce damaging
        winds, hail or even tornadoes.\\n\\nThe GLM data product consists of a hierarchy
        of earth-located lightning radiant energy measures including events, groups,
        and flashes:\\n\\n- Lightning events are detected by the instrument.\\n- Lightning
        groups are a collection of one or more lightning events that satisfy temporal
        and spatial coincidence thresholds.\\n- Similarly, lightning flashes are a
        collection of one or more lightning groups that satisfy temporal and spatial
        coincidence thresholds.\\n\\nThe product includes the relationship among lightning
        events, groups, and flashes, and the area coverage of lightning groups and
        flashes. The product also includes processing and data quality metadata, and
        satellite state and location information. \\n\\nThe NetCDF files are delivered
        to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\",\"item_assets\":{\"netcdf\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Original
        NetCDF4 file\"}},\"msft:region\":\"westeurope\",\"sci:citation\":\"GOES-R
        Algorithm Working Group and GOES-R Series Program, (2018): NOAA GOES-R Series
        Geostationary Lightning Mapper (GLM) Level 2 Lightning Detection: Events,
        Groups, and Flashes. NOAA National Centers for Environmental Information.
        doi:10.7289/V5KH0KK6.\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"goes\",\"msft:container\":\"noaa-goes-geoparquet\",\"stac_extensions\":[\"https://stac-extensions.github.io/goes/v1.0.0/schema.json\",\"https://stac-extensions.github.io/processing/v1.1.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"goeseuwest\",\"msft:short_description\":\"Continuous
        lightning detection over the Western Hemisphere from the Geostationary Lightning
        Mapper (GLM) instrument.\"},{\"id\":\"sentinel-1-grd\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"title\":\"Copernicus
        Sentinel data terms\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel
        1 Level-1 Ground Range Detected (GRD)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-1-grd.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Sentinel
        1 GRD\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-1-grd.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2014-10-10T00:28:21Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"ESA\",\"Copernicus\",\"Sentinel\",\"C-Band\",\"SAR\",\"GRD\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"platform\":[\"SENTINEL-1A\",\"SENTINEL-1B\",\"SENTINEL-1C\"],\"constellation\":[\"Sentinel-1\"],\"s1:resolution\":[\"full\",\"high\",\"medium\"],\"s1:orbit_source\":[\"DOWNLINK\",\"POEORB\",\"PREORB\",\"RESORB\"],\"sar:looks_range\":[5,6,3,2],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"sar:product_type\":[\"GRD\"],\"sar:looks_azimuth\":[1,6,2],\"sar:polarizations\":[[\"VV\",\"VH\"],[\"HH\",\"HV\"],[\"VV\"],[\"VH\"],[\"HH\"],[\"HV\"]],\"sar:frequency_band\":[\"C\"],\"s1:processing_level\":[\"1\"],\"sar:instrument_mode\":[\"IW\",\"EW\",\"SM\"],\"sar:center_frequency\":[5.405],\"sar:resolution_range\":[20,23,50,93,9],\"s1:product_timeliness\":[\"NRT-10m\",\"NRT-1h\",\"NRT-3h\",\"Fast-24h\",\"Off-line\",\"Reprocessing\"],\"sar:resolution_azimuth\":[22,23,50,87,9],\"sar:pixel_spacing_range\":[10,25,40,3.5],\"sar:observation_direction\":[\"right\"],\"sar:pixel_spacing_azimuth\":[10,25,40,3.5],\"sar:looks_equivalent_number\":[4.4,29.7,2.7,10.7,3.7],\"sat:platform_international_designator\":[\"2014-016A\",\"2016-025A\",\"0000-000A\"]},\"description\":\"The
        [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission
        is a constellation of two polar-orbiting satellites, operating day and night
        performing C-band synthetic aperture radar imaging. The Level-1 Ground Range
        Detected (GRD) products in this Collection consist of focused SAR data that
        has been detected, multi-looked and projected to ground range using the Earth
        ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected
        using the terrain height specified in the product general annotation. The
        terrain height used varies in azimuth but is constant in range (but can be
        different for each IW/EW sub-swath).\\n\\nGround range coordinates are the
        slant range coordinates projected onto the ellipsoid of the Earth. Pixel values
        represent detected amplitude. Phase information is lost. The resulting product
        has approximately square resolution pixels and square pixel spacing with reduced
        speckle at a cost of reduced spatial resolution.\\n\\nFor the IW and EW GRD
        products, multi-looking is performed on each burst individually. All bursts
        in all sub-swaths are then seamlessly merged to form a single, contiguous,
        ground range, detected image per polarization.\\n\\nFor more information see
        the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\\n\\n###
        Terrain Correction\\n\\nUsers might want to geometrically or radiometrically
        terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC
        Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc)
        collection is a global radiometrically terrain corrected dataset derived from
        Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any
        DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM).
        See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/)
        for more.\",\"item_assets\":{\"hh\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HH: horizontal
        transmit, horizontal receive\",\"description\":\"Amplitude of signal transmitted
        with horizontal polarization and received with horizontal polarization with
        radiometric terrain correction applied.\"},\"hv\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HV:
        horizontal transmit, vertical receive\",\"description\":\"Amplitude of signal
        transmitted with horizontal polarization and received with vertical polarization
        with radiometric terrain correction applied.\"},\"vh\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VH:
        vertical transmit, horizontal receive\",\"description\":\"Amplitude of signal
        transmitted with vertical polarization and received with horizontal polarization
        with radiometric terrain correction applied.\"},\"vv\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VV:
        vertical transmit, vertical receive\",\"description\":\"Amplitude of signal
        transmitted with vertical polarization and received with vertical polarization
        with radiometric terrain correction applied.\"},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Preview
        Image\",\"description\":\"An averaged, decimated preview image in PNG format.
        Single polarisation products are represented with a grey scale image. Dual
        polarisation products are represented by a single composite colour image in
        RGB with the red channel (R) representing the  co-polarisation VV or HH),
        the green channel (G) represents the cross-polarisation (VH or HV) and the
        blue channel (B) represents the ratio of the cross an co-polarisations.\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Manifest
        File\",\"description\":\"General product metadata in XML format. Contains
        a high-level textual description of the product and references to all of product's
        components, the product metadata, including the product identification and
        the resource references, and references to the physical location of each component
        file contained in the product.\"},\"schema-noise-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise
        Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise
        Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise
        Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise
        Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-product-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product
        Schema\",\"description\":\"Describes the main characteristics corresponding
        to the band: state of the platform during acquisition, image properties, Doppler
        information, geographic location, etc.\"},\"schema-product-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product
        Schema\",\"description\":\"Describes the main characteristics corresponding
        to the band: state of the platform during acquisition, image properties, Doppler
        information, geographic location, etc.\"},\"schema-product-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product
        Schema\",\"description\":\"Describes the main characteristics corresponding
        to the band: state of the platform during acquisition, image properties, Doppler
        information, geographic location, etc.\"},\"schema-product-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product
        Schema\",\"description\":\"Describes the main characteristics corresponding
        to the band: state of the platform during acquisition, image properties, Doppler
        information, geographic location, etc.\"},\"schema-calibration-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration
        Schema\",\"description\":\"Calibration metadata including calibration information
        and the beta nought, sigma nought, gamma and digital number look-up tables
        that can be used for absolute product calibration.\"},\"schema-calibration-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration
        Schema\",\"description\":\"Calibration metadata including calibration information
        and the beta nought, sigma nought, gamma and digital number look-up tables
        that can be used for absolute product calibration.\"},\"schema-calibration-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration
        Schema\",\"description\":\"Calibration metadata including calibration information
        and the beta nought, sigma nought, gamma and digital number look-up tables
        that can be used for absolute product calibration.\"},\"schema-calibration-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration
        Schema\",\"description\":\"Calibration metadata including calibration information
        and the beta nought, sigma nought, gamma and digital number look-up tables
        that can be used for absolute product calibration.\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-1\",\"msft:container\":\"s1-grd\",\"stac_extensions\":[\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\",\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"sentinel1euwest\",\"msft:short_description\":\"Sentinel-1
        Level-1 Ground Range Detected (GRD) products consist of focused SAR data that
        has been detected, multi-looked and projected to ground range using an Earth
        ellipsoid model.\",\"msft:region\":\"westeurope\"},{\"id\":\"noaa-climate-normals-gridded\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals\",\"type\":\"text/html\",\"title\":\"NOAA
        U.S. Climate Normals Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.noaa.gov/information-technology/open-data-dissemination\",\"type\":\"text/html\",\"title\":\"NOAA
        Open Data Dissemination\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf\",\"type\":\"application/pdf\",\"title\":\"Readme
        for Monthly Gridded Normals\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf\",\"type\":\"application/pdf\",\"title\":\"Documentation
        for Monthly Gridded Normals\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA
        US Gridded Climate Normals (Cloud-Optimized GeoTIFF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-gridded-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NOAA
        U.S. Gridded Climate Normals (Cloud-Optimized GeoTIFF) Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-climate-normals-gridded.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.708333,24.541666,-66.999995,49.375001]]},\"temporal\":{\"interval\":[[\"1901-01-01T00:00:00Z\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"NOAA\",\"Climate
        Normals\",\"Weather\",\"Surface Observations\",\"Climatology\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA
        National Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"noaa_climate_normals:period\":[\"1901-2000\",\"1991-2020\",\"2006-2020\"],\"noaa_climate_normals:frequency\":[\"daily\",\"monthly\",\"seasonal\",\"annual\"]},\"description\":\"The
        [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027)
        provide a continuous grid of temperature and precipitation data across the
        contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid
        dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid),
        and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore
        match that of NClimGrid. Monthly, seasonal, and annual gridded normals are
        computed from simple averages of the NClimGrid data and are provided for three
        time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded
        normals are smoothed for a smooth transition from one day to another and are
        provided for two time-periods: 1991\u20132020, and 2006\u20132020.\\n\\nNOAA
        produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en)
        (WMO), of which the United States is a member. The WMO requires each member
        nation to compute 30-year meteorological quantity averages at least every
        30 years, and recommends an update each decade, in part to incorporate newer
        weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in
        a series of decadal normals first produced in the 1950s. \\n\\nThis Collection
        contains gridded data for the following frequencies and time periods:\\n\\n-
        Annual, seasonal, and monthly normals\\n    - 100-year (1901\u20132000)\\n
        \   - 30-year (1991\u20132020)\\n    - 15-year (2006\u20132020)\\n- Daily
        normals\\n    - 30-year (1991\u20132020)\\n    - 15-year (2006\u20132020)\\n\\nThe
        data in this Collection have been converted from the original NetCDF format
        to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered
        to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\n##
        STAC Metadata\\n\\nThe STAC items in this collection contain several custom
        fields that can be used to further filter the data.\\n\\n* `noaa_climate_normals:period`:
        Climate normal time period. This can be \\\"1901-2000\\\", \\\"1991-2020\\\",
        or \\\"2006-2020\\\".\\n* `noaa_climate_normals:frequency`: Climate normal
        temporal interval (frequency). This can be \\\"daily\\\", \\\"monthly\\\",
        \\\"seasonal\\\" , or \\\"annual\\\"\\n* `noaa_climate_normals:time_index`:
        Time step index, e.g., month of year (1-12).\\n\\nThe `description` field
        of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions
        are\\n\\n* annual: \\\"Annual precipitation normals from monthly precipitation
        normal values\\\"\\n* seasonal: \\\"Seasonal precipitation normals (WSSF)
        from monthly normals\\\"\\n* monthly: \\\"Monthly precipitation normals from
        monthly precipitation values\\\"\\n* daily: \\\"Precipitation normals from
        daily averages\\\"\\n\\nCheck the assets on individual items for the appropriate
        description.\\n\\nThe STAC keys for most assets consist of two abbreviations.
        A \\\"variable\\\":\\n\\n\\n| Abbreviation |               Description                |\\n|
        ------------ | ---------------------------------------- |\\n| prcp         |
        Precipitation over the time period       |\\n| tavg         | Mean temperature
        over the time period    |\\n| tmax         | Maximum temperature over the
        time period |\\n| tmin         | Minimum temperature over the time period
        |\\n\\nAnd an \\\"aggregation\\\":\\n\\n| Abbreviation |                                  Description
        \                                  |\\n| ------------ | ------------------------------------------------------------------------------
        |\\n| max          | Maximum of the variable over the time period                                   |\\n|
        min          | Minimum of the variable over the time period                                   |\\n|
        std          | Standard deviation of the value over the time period                           |\\n|
        flag         | An count of the number of inputs (months, years, etc.) to calculate
        the normal |\\n| norm         | The normal for the variable over the time
        period                               |\\n\\nSo, for example, `prcp_max` for
        monthly data is the \\\"Maximum values of all input monthly precipitation
        normal values\\\".\\n\",\"item_assets\":{\"prcp_max\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_min\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_std\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_max\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_min\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_std\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_max\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_min\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_std\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_max\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_min\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_std\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_flag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_flag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_flag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_flag\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"m2dprcp_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"description\":\"Month-To-Day
        Precipitation normals\",\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"y2dprcp_norm\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"description\":\"Year-To-Day
        Precipitation normals\",\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]}},\"msft:region\":\"eastus\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-climate-normals\",\"msft:container\":\"gridded-normals-cogs\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\"],\"msft:storage_account\":\"noaanormals\",\"msft:short_description\":\"Gridded
        Climate Normals for the contiguous United States in Cloud Optimized GeoTIFF
        (COG) format.\"},{\"id\":\"aster-l1t\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/core-science-systems/hdds/data-policy\",\"title\":\"Public
        Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\",\"type\":\"text/html\",\"title\":\"Human
        readable dataset overview and reference\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ASTER
        L1T\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"ASTER
        L1T\"},\"geoparquet-items\":{\"href\":\"abfs://items/aster-l1t.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-03-04T12:00:00Z\",\"2006-12-31T12:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"ASTER\",\"USGS\",\"NASA\",\"Satellite\",\"Global\"],\"providers\":[{\"url\":\"https://terra.nasa.gov/about/terra-instruments/aster\",\"name\":\"NASA\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/aster-overview/\",\"name\":\"USGS\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[15,30,90],\"eo:bands\":[{\"gsd\":15,\"name\":\"VNIR_Band1\",\"common_name\":\"green\",\"description\":\"visible
        yellow/green\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"gsd\":15,\"name\":\"VNIR_Band2\",\"common_name\":\"red\",\"description\":\"visible
        red\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"gsd\":15,\"name\":\"VNIR_Band3N\",\"common_name\":\"nir08\",\"description\":\"near
        infrared\",\"center_wavelength\":0.82,\"full_width_half_max\":0.08},{\"gsd\":30,\"name\":\"SWIR_Band4\",\"common_name\":\"swir16\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":1.65,\"full_width_half_max\":0.1},{\"gsd\":30,\"name\":\"SWIR_Band5\",\"common_name\":\"swir22\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.165,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band6\",\"common_name\":\"swir22\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.205,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band7\",\"common_name\":\"swir22\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.26,\"full_width_half_max\":0.05},{\"gsd\":30,\"name\":\"SWIR_Band8\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.339,\"full_width_half_max\":0.07},{\"gsd\":30,\"name\":\"SWIR_Band9\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.395,\"full_width_half_max\":0.07},{\"gsd\":90,\"name\":\"TIR_Band10\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":8.3,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band11\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":8.65,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band12\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":9.11,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band13\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":10.6,\"full_width_half_max\":0.7},{\"gsd\":90,\"name\":\"TIR_Band14\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":11.3,\"full_width_half_max\":0.7}],\"platform\":[\"terra\"],\"instruments\":[\"aster\"]},\"description\":\"The
        [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument,
        launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999,
        provides multispectral images of the Earth at 15m-90m resolution.  ASTER images
        provide information about land surface temperature, color, elevation, and
        mineral composition.\\n\\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/)
        data from 2000-2006.  L1T images have been terrain-corrected and rotated to
        a north-up UTM projection.  Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/)
        format.\\n\",\"item_assets\":{\"TIR\":{\"roles\":[\"data\"],\"title\":\"TIR
        Swath data\",\"eo:bands\":[{\"gsd\":90,\"name\":\"TIR_Band10\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":8.3,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band11\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":8.65,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band12\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":9.11,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band13\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":10.6,\"full_width_half_max\":0.7},{\"gsd\":90,\"name\":\"TIR_Band14\",\"common_name\":\"lwir\",\"description\":\"thermal
        infrared\",\"center_wavelength\":11.3,\"full_width_half_max\":0.7}],\"raster:bands\":[{\"name\":\"TIR_Band10\",\"nodata\":0,\"data_type\":\"uint16\",\"spatial_resolution\":90},{\"name\":\"TIR_Band11\",\"nodata\":0,\"data_type\":\"uint16\",\"spatial_resolution\":90},{\"name\":\"TIR_Band12\",\"nodata\":0,\"data_type\":\"uint16\",\"spatial_resolution\":90},{\"name\":\"TIR_Band13\",\"nodata\":0,\"data_type\":\"uint16\",\"spatial_resolution\":90},{\"name\":\"TIR_Band14\",\"nodata\":0,\"data_type\":\"uint16\",\"spatial_resolution\":90}]},\"xml\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"XML
        metadata\"},\"SWIR\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR
        Swath data\",\"eo:bands\":[{\"gsd\":30,\"name\":\"SWIR_Band4\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":1.65,\"full_width_half_max\":0.1},{\"gsd\":30,\"name\":\"SWIR_Band5\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.165,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band6\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.205,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band7\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.26,\"full_width_half_max\":0.05},{\"gsd\":30,\"name\":\"SWIR_Band8\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.339,\"full_width_half_max\":0.07},{\"gsd\":30,\"name\":\"SWIR_Band9\",\"common_name\":\"swir\",\"description\":\"short-wave
        infrared\",\"center_wavelength\":2.395,\"full_width_half_max\":0.07}],\"raster:bands\":[{\"name\":\"SWIR_Band4\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30},{\"name\":\"SWIR_Band5\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30},{\"name\":\"SWIR_Band6\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30},{\"name\":\"SWIR_Band7\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30},{\"name\":\"SWIR_Band8\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30},{\"name\":\"SWIR_Band9\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"VNIR\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VNIR
        Swath data\",\"eo:bands\":[{\"gsd\":15,\"name\":\"VNIR_Band1\",\"common_name\":\"yellow/green\",\"description\":\"visible
        yellow/green\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"gsd\":15,\"name\":\"VNIR_Band2\",\"common_name\":\"red\",\"description\":\"visible
        red\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"gsd\":15,\"name\":\"VNIR_Band3N\",\"common_name\":\"near
        infrared\",\"description\":\"near infrared\",\"center_wavelength\":0.82,\"full_width_half_max\":0.08}],\"raster:bands\":[{\"name\":\"VNIR_Band1\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":15},{\"name\":\"VNIR_Band2\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":15},{\"name\":\"VNIR_Band3N\",\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":15}]},\"qa-txt\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"QA
        browse file\",\"description\":\"Geometric quality assessment report.\"},\"qa-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"QA
        browse file\",\"description\":\"Single-band black and white reduced resolution
        browse overlaid with red, green, and blue (RGB) markers for GCPs used during
        the geometric verification quality check.\"},\"tir-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"Standalone
        reduced resolution TIR\"},\"vnir-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"VNIR
        browse file\",\"description\":\"Standalone reduced resolution VNIR\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"aster\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"astersa\",\"msft:short_description\":\"The
        ASTER instrument, launched on-board NASA's Terra satellite in 1999, provides
        multispectral images of the Earth at 15m-90m resolution.  This dataset contains
        ASTER data from 2000-2006.\",\"msft:region\":\"westeurope\"},{\"id\":\"cil-gdpcir-cc-by-sa\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa\"},{\"rel\":\"license\",\"href\":\"https://spdx.org/licenses/CC-BY-SA-4.0.html\",\"type\":\"text/html\",\"title\":\"Creative
        Commons Attribution Share Alike 4.0 International\"},{\"rel\":\"cite-as\",\"href\":\"https://zenodo.org/record/6403794\",\"type\":\"text/html\"},{\"rel\":\"describedby\",\"href\":\"https://github.com/ClimateImpactLab/downscaleCMIP6/\",\"type\":\"text/html\",\"title\":\"Project
        homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL
        Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"1950-01-01T00:00:00Z\",\"2100-12-31T00:00:00Z\"]]}},\"license\":\"CC-BY-SA-4.0\",\"sci:doi\":\"10.5194/egusphere-2022-1513\",\"keywords\":[\"CMIP6\",\"Climate
        Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate
        Impact Lab\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"cmip6:variable\":[\"pr\",\"tasmax\",\"tasmin\"],\"cmip6:source_id\":[\"CanESM5\"],\"cmip6:experiment_id\":[\"historical\",\"ssp126\",\"ssp245\",\"ssp370\",\"ssp585\"],\"cmip6:institution_id\":[\"BCC\",\"CAS\",\"CCCma\",\"CMCC\",\"CSIRO\",\"CSIRO-ARCCSS\",\"DKRZ\",\"EC-Earth-Consortium\",\"INM\",\"MIROC\",\"MOHC\",\"MPI-M\",\"NCC\",\"NOAA-GFDL\",\"NUIST\"]},\"description\":\"The
        World Climate Research Programme's [6th Coupled Model Intercomparison Project
        (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an
        enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe
        [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)
        dataset makes this modeling more applicable to understanding the impacts of
        changes in the climate on humans and society with two key developments: trend-preserving
        bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)
        provides global, daily minimum and maximum air temperature at the surface
        (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)
        corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5
        scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n##
        Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary
        Computer. The dataset is made of of three collections, distinguished by data
        license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n*
        [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n*
        [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\\n\\nEach
        modeling center with bias corrected and downscaled data in this collection
        falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection)
        to see which model is in each collection, and see the section below on [Citing,
        Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project)
        for citations and additional information about each license.\\n\\n## Data
        format & contents\\n\\nThe data is stored as partitioned zarr stores (see
        [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which
        includes thousands of data and metadata files covering the full time span
        of the experiment. Historical zarr stores contain just over 50 GB, while SSP
        zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with
        dimensions time (daily datetime), lat (float latitude), and lon (float longitude).
        The data is chunked at each interval of 365 days and 90 degree interval of
        latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each
        chunk occupying approximately 179MB in memory.\\n\\nHistorical data is daily,
        excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,
        excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,
        depending on data availability in the source GCM.\\n\\nThe spatial domain
        covers all 0.25-degree grid cells, indexed by the grid center, with grid edges
        on the quarter-degree, using a -180 to 180 longitude convention. Thus, the
        \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D
        coordinate extends from -89.875 to 89.875, with intermediate values at each
        0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n##
        Available institutions, models, and scenarios by license collection\\n\\n|
        Modeling institution |   Source model    |           Available experiments
        \           |   License collection   |\\n| -------------------- | -----------------
        | ------------------------------------------ | ---------------------- |\\n|
        CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5
        \          | Public domain datasets |\\n| INM                  | INM-CM4-8
        \        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets
        |\\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0,
        and SSP5-8.5 | Public domain datasets |\\n| BCC                  | BCC-CSM2-MR
        \      | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\\n|
        CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5
        \    | CC-BY-40]              |\\n| CMCC                 | CMCC-ESM2         |
        ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40]              |\\n|
        CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      |
        CC-BY-40]              |\\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6,
        SSP2-4.5, and SSP3-7.0           | CC-BY-40]              |\\n| MIROC                |
        MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]
        \             |\\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5,
        SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\\n| MOHC                 |
        HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]
        \             |\\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5,
        SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\\n| MPI-M                |
        MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]
        \             |\\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and
        SSP5-8.5                      | CC-BY-40]              |\\n| NCC                  |
        NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]
        \             |\\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5,
        SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\\n| NOAA-GFDL            |
        GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40]
        \             |\\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5,
        SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\\n| NUIST                |
        NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]
        \             |\\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5,
        ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\\n| EC-Earth-Consortium
        \ | EC-Earth3-AerChem | ssp370                                     | CC-BY-40]
        \             |\\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and
        ssp585                          | CC-BY-40]              |\\n| EC-Earth-Consortium
        \ | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]
        \             |\\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5,
        ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\\n| CCCma                |
        CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-SA-40]
        \          |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6
        precipitation data was available. Therefore, we provide `tasmin` and `tamax`
        for this model and experiment, but not `pr`. All other model/experiment combinations
        in the above table include all three variables.\\n\\n[^2]: The institution
        which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while
        the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution
        component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`
        scenarios.\\n\\n## Project methods\\n\\nThis project makes use of statistical
        bias correction and downscaling algorithms, which are specifically designed
        to accurately represent changes in the extremes. For this reason, we selected
        Quantile Delta Mapping (QDM), following the method introduced by [Cannon et
        al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific
        trends from the GCM while fitting the full distribution for a given day-of-year
        to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored
        to increase spatial resolution while preserving extreme behavior, Quantile-Preserving
        Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide
        a robust means to handle both the central and tail behavior seen in climate
        model output, while aligning the full distribution to a state-of-the-art reanalysis
        dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor
        further documentation, see [Global downscaled projections for climate impacts
        research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)
        (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced
        by this project\\n\\nProjects making use of the data produced as part of the
        Climate Impact Lab Global Downscaled Projections for Climate Impacts Research
        (CIL GDPCIR) project are requested to cite both this project and the source
        datasets from which these results are derived. Additionally, the use of data
        derived from some GCMs *requires* citations, and some modeling centers impose
        licensing restrictions & requirements on derived works. See each GCM's license
        info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers
        are requested to cite this project in derived works. Our method documentation
        paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S.
        B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,
        R. E.: Global downscaled projections for climate impacts research (GDPCIR):
        preserving extremes for modeling future climate impacts, EGUsphere [preprint],
        https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository
        may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster
        Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:
        (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally,
        we request you cite the historical dataset used in bias correction and downscaling,
        ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\n\\n>
        Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:
        1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n>
        Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.
        Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed
        on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n>
        Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus
        Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,
        2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n###
        GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available
        through the Earth System Grid Federation (ESGF) are subject to Creative Commons
        [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA
        4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate
        Impact Lab has reached out to each of the modeling institutions to request
        waivers from these terms so the outputs of this project may be used with fewer
        restrictions, and has been granted permission to release the data using the
        licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following
        bias corrected and downscaled model simulations are available in the public
        domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\n\\n*
        **FGOALS-g3**\\n\\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output
        prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6
        ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0
        version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version
        20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\n\\n\\n*
        **INM-CM4-8**\\n\\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8
        model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\n\\n
        \ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5
        version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n####
        CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations
        are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
        Note that this license requires citation of the source model output (included
        here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\n\\n*
        **ACCESS-CM2**\\n\\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,
        Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,
        Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;
        Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,
        Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,
        Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,
        Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;
        Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2
        model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,
        Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,
        Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;
        Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,
        Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,
        Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,
        Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;
        Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output
        prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\n\\n
        \ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;
        Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;
        Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,
        Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5
        model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,
        Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;
        Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,
        Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared
        for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\n\\n\\n*
        **BCC-CSM2-MR**\\n\\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;
        Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;
        Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.
        Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,
        Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,
        Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;
        SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\n\\n\\n*
        **CMCC-CM2-SR5**\\n\\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC
        CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5
        version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\n\\n\\n*
        **CMCC-ESM2**\\n\\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme
        **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version
        20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n,
        Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;
        SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\n\\n\\n*
        **EC-Earth3-AerChem**\\n\\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.
        Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\n\\n\\n*
        **EC-Earth3-CC**\\n\\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium
        EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\n\\n\\n*
        **EC-Earth3-Veg-LR**\\n\\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;
        SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\n\\n\\n*
        **EC-Earth3-Veg**\\n\\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\n\\n\\n*
        **EC-Earth3**\\n\\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\n\\n\\n*
        **GFDL-CM4**\\n\\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,
        Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,
        Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,
        Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen
        M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,
        Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;
        Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;
        Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;
        Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,
        P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.
        *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;
        Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,
        Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;
        Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,
        Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,
        Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;
        Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;
        Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,
        Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4
        model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n
        \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris;
        McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;
        Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,
        Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;
        Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,
        David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,
        Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,
        John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry
        W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;
        Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4
        model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;
        Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,
        John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,
        Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL
        GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\n\\n\\n*
        **HadGEM3-GC31-LL**\\n\\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;
        Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output
        prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for
        CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;
        SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\n\\n\\n*
        **MIROC-ES2L**\\n\\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,
        Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,
        Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,
        Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,
        Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.
        Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;
        Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;
        Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,
        Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,
        Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.
        Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\n\\n\\n*
        **MIROC6**\\n\\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.
        *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.
        *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\n\\n\\n*
        **MPI-ESM1-2-HR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;
        Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,
        Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,
        J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;
        Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,
        Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;
        Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Schupfner, Martin; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;
        Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,
        Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;
        Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg;
        Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,
        Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;
        Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;
        Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang;
        Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,
        Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;
        Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich
        **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.
        Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\n\\n\\n*
        **MPI-ESM1-2-LR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,
        Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;
        Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de
        Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;
        Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,
        Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;
        Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,
        Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco;
        Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,
        Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;
        von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin;
        Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,
        Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,
        Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,
        Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von
        Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,
        Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;
        Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR
        model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n
        \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model
        output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\n\\n\\n*
        **NorESM2-LM**\\n\\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk
        Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model
        output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\n\\n\\n*
        **NorESM2-MM**\\n\\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland,
        \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model
        output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\n\\n\\n*
        **UKESM1-0-LL**\\n\\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,
        Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.
        *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Good, Peter; Sellar, Alistair; Tang, Yongming;
        Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy
        **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;
        SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n\\n####
        CC-BY-SA-4.0\\n\\nThe following bias corrected and downscaled model simulations
        are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International
        License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this
        license requires citation of the source model output (included here) and requires
        that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/
        for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\\n\\n*
        **CanESM5**\\n\\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav
        V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora,
        Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad;
        Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim,
        Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**.
        *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav
        V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora,
        Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad;
        Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim,
        Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**.
        *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n##
        Acknowledgements\\n\\nThis work is the result of many years worth of work
        by members of the [Climate Impact Lab](https://impactlab.org), but would not
        have been possible without many contributions from across the wider scientific
        and computing communities.\\n\\nSpecifically, we would like to acknowledge
        the World Climate Research Programme's Working Group on Coupled Modeling,
        which is responsible for CMIP, and we would like to thank the climate modeling
        groups for producing and making their model output available. We would particularly
        like to thank the modeling institutions whose results are included as an input
        to this repository (listed above) for their contributions to the CMIP6 project
        and for responding to and granting our requests for license waivers.\\n\\nWe
        would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)
        (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))
        and Google Cloud and the Google Public Datasets program for making the [CMIP6
        Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)
        possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),
        [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),
        [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),
        and [Charles Stern](https://github.com/cisaacstern) for the huge amount of
        work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,
        analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the
        [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)
        ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in
        particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),
        and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM
        bias correction method in the xclim python package, supporting our QPLAD implementation
        into the package, and ongoing support in integrating dask into downscaling
        workflows. For method advice and useful conversations, we would like to thank
        Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n##
        Financial support\\n\\nThis research has been supported by The Rockefeller
        Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n*
        CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n*
        Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate
        Impact Lab homepage: [impactlab.org](https://impactlab.org)\",\"item_assets\":{\"pr\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Precipitation\",\"description\":\"Precipitation\"},\"tasmax\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily
        Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface
        Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily
        Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface
        Air Temperature\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"cil-gdpcir\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"mm
        day-1\",\"attrs\":{\"units\":\"mm day-1\"},\"dimensions\":[\"time\",\"lat\",\"lon\"]},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum
        near-surface (usually, 2 meter) air temperature (add cell_method attribute
        'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area:
        mean time: maximum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMX\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"minimum
        near-surface (usually, 2 meter) air temperature (add cell_method attribute
        'time: min')\",\"long_name\":\"Daily Minimum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area:
        mean time: minimum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMN\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily
        Minimum Near-Surface Air Temperature\"}},\"msft:container\":\"cil-gdpcir\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[-89.875,89.875],\"reference_system\":\"epsg:4326\"},\"lon\":{\"axis\":\"x\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[-179.875,179.875],\"reference_system\":\"epsg:4326\"},\"time\":{\"step\":\"P1DT0H0M0S\",\"type\":\"temporal\",\"extent\":[\"1950-01-01T12:00:00Z\",\"2100-12-31T12:00:00Z\"],\"description\":\"time\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\"msft:storage_account\":\"rhgeuwest\",\"msft:short_description\":\"Climate
        Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)\",\"msft:region\":\"westeurope\"},{\"id\":\"naip\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip\"},{\"rel\":\"license\",\"href\":\"https://www.fsa.usda.gov/help/policies-and-links/\",\"title\":\"Public
        Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/naip\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NAIP:
        National Agriculture Imagery Program\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/naip.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NAIP
        thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/naip.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.784,24.744,-66.951,49.346],[-156.003,19.059,-154.809,20.127],[-67.316,17.871,-65.596,18.565],[-64.94,17.622,-64.56,17.814]]},\"temporal\":{\"interval\":[[\"2010-01-01T00:00:00Z\",\"2023-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"NAIP\",\"Aerial\",\"Imagery\",\"USDA\",\"AFPO\",\"Agriculture\",\"United
        States\"],\"providers\":[{\"url\":\"https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/\",\"name\":\"USDA
        Farm Service Agency\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[0.3,0.6,1.0],\"eo:bands\":[{\"name\":\"Red\",\"common_name\":\"red\",\"description\":\"visible
        red\"},{\"name\":\"Green\",\"common_name\":\"green\",\"description\":\"visible
        green\"},{\"name\":\"Blue\",\"common_name\":\"blue\",\"description\":\"visible
        blue\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}]},\"description\":\"The
        [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/)
        (NAIP) \\nprovides U.S.-wide, high-resolution aerial imagery, with four spectral
        bands (R, G, B, IR). \\nNAIP is administered by the [Aerial Field Photography
        Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/)
        (AFPO) \\nwithin the [US Department of Agriculture](https://www.usda.gov/)
        (USDA). \\nData are captured at least once every three years for each state.
        \\nThis dataset represents NAIP data from 2010-present, in [cloud-optimized
        GeoTIFF](https://www.cogeo.org/) format.\\nYou can visualize the coverage
        of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/).
        \\n\",\"item_assets\":{\"image\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"RGBIR COG tile\",\"eo:bands\":[{\"name\":\"Red\",\"common_name\":\"red\"},{\"name\":\"Green\",\"common_name\":\"green\"},{\"name\":\"Blue\",\"common_name\":\"blue\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}]},\"metadata\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"FGDC
        Metdata\"},\"thumbnail\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"naip\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"naipeuwest\",\"msft:short_description\":\"NAIP
        provides US-wide, high-resolution aerial imagery.  This dataset includes NAIP
        images from 2010 to the present.\"},{\"id\":\"io-lulc-9-class\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class\"},{\"rel\":\"related\",\"href\":\"https://livingatlas.arcgis.com/landcover/\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC
        BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"10m
        Annual Land Use Land Cover (9-class) V1\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-9-class.png\",\"title\":\"10m
        Annual Land Use Land Cover (9-class)\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/io-lulc-9-class.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2017-01-01T00:00:00Z\",\"2023-01-01T00:00:00Z\"]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"Global\",\"Land
        Cover\",\"Land Use\",\"Sentinel\"],\"providers\":[{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"licensor\"]},{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact
        Observatory\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"raster:bands\":[{\"nodata\":0,\"spatial_resolution\":10}]},\"description\":\"__Note__:
        _A new version of this item is available for your use. This mature version
        of the map remains available for use in existing applications. This item will
        be retired in December 2024. There is 2023 data available in the newer [9-class
        v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\\n\\nTime
        series of annual global maps of land use and land cover (LULC). It currently
        has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery
        at 10m resolution. Each map is a composite of LULC predictions for 9 classes
        throughout the year in order to generate a representative snapshot of each
        year.\\n\\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/),
        who used billions of human-labeled pixels (curated by the National Geographic
        Society) to train a deep learning model for land classification. The global
        map was produced by applying this model to the Sentinel-2 annual scene collections
        on the Planetary Computer. Each of the maps has an assessed average accuracy
        of over 75%.\\n\\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc)
        and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single
        Rangeland class (class 11). The original Esri 2020 Land Cover collection uses
        10 classes (Grass and Scrub separate) and an older version of the underlying
        deep learning model.  The Esri 2020 Land Cover map was also produced by Impact
        Observatory.  The map remains available for use in existing applications.
        New applications should use the updated version of 2020 once it is available
        in this collection, especially when using data from multiple years of this
        time series, to ensure consistent classification.\\n\\nAll years are available
        under a Creative Commons BY-4.0.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global
        land cover data\",\"file:values\":[{\"values\":[0],\"summary\":\"No Data\"},{\"values\":[1],\"summary\":\"Water\"},{\"values\":[2],\"summary\":\"Trees\"},{\"values\":[4],\"summary\":\"Flooded
        vegetation\"},{\"values\":[5],\"summary\":\"Crops\"},{\"values\":[7],\"summary\":\"Built
        area\"},{\"values\":[8],\"summary\":\"Bare ground\"},{\"values\":[9],\"summary\":\"Snow/ice\"},{\"values\":[10],\"summary\":\"Clouds\"},{\"values\":[11],\"summary\":\"Rangeland\"}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"io-land-cover\",\"msft:container\":\"io-lulc\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/label/v1.0.0/schema.json\",\"https://stac-extensions.github.io/file/v2.1.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"Global
        land cover information with 9 classes for 2017-2022 at 10m resolution\"},{\"id\":\"io-biodiversity\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC
        BY 4.0\"},{\"rel\":\"about\",\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf\",\"type\":\"application/pdf\",\"title\":\"Technical
        White Paper\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-biodiversity\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Biodiversity
        Intactness\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-biodiversity-thumb.png\",\"title\":\"Biodiversity
        Intactness\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/io-biodiversity.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2017-01-01T00:00:00Z\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"Global\",\"Biodiversity\"],\"providers\":[{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact
        Observatory\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://www.vizzuality.com/\",\"name\":\"Vizzuality\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"version\":[\"v1\"]},\"description\":\"Generated
        by [Impact Observatory](https://www.impactobservatory.com/), in collaboration
        with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial
        Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\\n\\nMaps
        depicting the intactness of global biodiversity have become a critical tool
        for spatial planning and management, monitoring the extent of biodiversity
        across Earth, and identifying critical remaining intact habitat. Yet, these
        maps are often years out of date by the time they are available to scientists
        and policy-makers. The datasets in this STAC Collection build on past studies
        that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579)
        of spatially referenced observations of biodiversity across 32,000 sites from
        over 750 studies. The approach differs from previous work by modeling the
        relationship between observed biodiversity metrics and contemporary, global,
        geospatial layers of human pressures, with the intention of providing a high
        resolution monitoring product into the future.\\n\\nBiodiversity intactness
        is estimated as a combination of two metrics: Abundance, the quantity of individuals,
        and Compositional Similarity, how similar the composition of species is to
        an intact baseline. Linear mixed effects models are fit to estimate the predictive
        capacity of spatial datasets of human pressures on each of these metrics and
        project results spatially across the globe. These methods, as well as comparisons
        to other leading datasets and guidance on interpreting results, are further
        explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf)
        entitled \u201CGlobal 100m Projections of Biodiversity Intactness for the
        years 2017-2020.\u201D\\n\\nAll years are available under a Creative Commons
        BY-4.0 license.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Biodiversity Intactness\",\"description\":\"Terrestrial
        biodiversity intactness at 100m resolution\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"float32\",\"spatial_resolution\":100}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"impact\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"pcdata01euw\",\"msft:short_description\":\"Global
        terrestrial biodiversity intactness at 100m resolution for years 2017-2020\"},{\"id\":\"noaa-cdr-sea-surface-temperature-whoi\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi\"},{\"rel\":\"license\",\"href\":\"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea%20Surface%20Temperature%20-%20WHOI/UseAgreement_01B-27a.pdf\",\"type\":\"application/pdf\",\"title\":\"NOAA
        CDR Sea Surface Temperature - WHOI Use Agreement\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/products/climate-data-records/sea-surface-temperature-whoi\",\"type\":\"text/html\",\"title\":\"Sea
        Surface Temperature - WHOI CDR\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.7289/V5FB510W\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-sea-surface-temperature-whoi\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sea
        Surface Temperature - WHOI CDR\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-sea-surface-temperature-whoi-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Sea
        Surface Temperature - WHOI CDR thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-cdr-sea-surface-temperature-whoi.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-90,180,90]]},\"temporal\":{\"interval\":[[\"1988-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.7289/V5FB510W\",\"keywords\":[\"Global\",\"Climate\",\"NOAA\",\"Ocean\",\"Temperature\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"National
        Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"],\"description\":\"NCEI
        is the Nation's leading authority for environmental data, and manage one of
        the largest archives of atmospheric, coastal, geophysical, and oceanic research
        in the world. NCEI contributes to the NESDIS mission by developing new products
        and services that span the science disciplines and enable better data discovery.\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"The
        Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate
        Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean
        Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data
        are produced through modeling the diurnal variability in combination with
        AVHRR SST observations. The final record is output to a 3-hourly 0.25\xB0
        resolution grid over the global ice-free oceans from January 1988\u2014present.\\n\\nThese
        Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered
        to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\nFor
        the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\\n\",\"item_assets\":{\"fill_missing_qc\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"sea_surface_temperature\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree
        Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]}},\"msft:region\":\"eastus\",\"sci:citation\":\"Clayson,
        Carol Anne; Brown, Jeremiah; and NOAA CDR Program (2016). NOAA Climate Data
        Record (CDR) of Sea Surface Temperature - WHOI, Version 2. NOAA National Climatic
        Data Center. doi:10.7289/V5FB510W\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-cdr\",\"msft:container\":\"sea-surface-temperature-whoi\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\"],\"msft:storage_account\":\"noaacdr\",\"msft:short_description\":\"The
        Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate
        Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean
        Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data
        are produced through modeling the diurnal variability in combination with
        AVHRR SST observations.\"},{\"id\":\"noaa-cdr-ocean-heat-content\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content\"},{\"rel\":\"license\",\"href\":\"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Ocean_Heat_Content/UseAgreement_01B-41.pdf\",\"type\":\"application/pdf\",\"title\":\"NOAA
        CDR Ocean Heat Content Use Agreement\"},{\"rel\":\"about\",\"href\":\"https://www.ncei.noaa.gov/products/climate-data-records/global-ocean-heat-content\",\"type\":\"text/html\",\"title\":\"Global
        Ocean Heat Content CDR\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.7289/v53f4mvp\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-ocean-heat-content\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Global
        Ocean Heat Content CDR\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-ocean-heat-content-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Global
        Ocean Heat Content CDR thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-cdr-ocean-heat-content.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-90.0,180.0,90.0]]},\"temporal\":{\"interval\":[[\"1972-03-01T00:00:00Z\",\"2022-03-31T23:59:59Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.7289/v53f4mvp\",\"keywords\":[\"Global\",\"Climate\",\"NOAA\",\"Temperature\",\"Ocean\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"National
        Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"],\"description\":\"NCEI
        is the Nation's leading authority for environmental data, and manage one of
        the largest archives of atmospheric, coastal, geophysical, and oceanic research
        in the world. NCEI contributes to the NESDIS mission by developing new products
        and services that span the science disciplines and enable better data discovery.\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"summaries\":{\"noaa_cdr:interval\":[\"monthly\",\"seasonal\",\"yearly\",\"pentadal\"],\"noaa_cdr:max_depth\":[100,700,2000]},\"description\":\"The
        Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content
        anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal
        (five-yearly) scales. This CDR quantifies ocean heat content change over time,
        which is an essential metric for understanding climate change and the Earth's
        energy budget. It provides time-series for multiple depth ranges in the global
        ocean and each of the major basins (Atlantic, Pacific, and Indian) divided
        by hemisphere (Northern, Southern).\\n\\nThese Cloud Optimized GeoTIFFs (COGs)
        were created from NetCDF files which are delivered to Azure as part of the
        [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\nFor
        the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\\n\",\"item_assets\":{\"heat_content\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Ocean
        Heat Content anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"10^18 joules\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_salinity\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean
        salinity anomalies from WOA09\",\"raster:bands\":[{\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_temperature\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean
        temperature anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"degrees Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_halosteric_sea_level\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean
        halosteric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_thermosteric_sea_level\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean
        thermosteric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_total_steric_sea_level\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean
        total steric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]}},\"msft:region\":\"eastus\",\"sci:citation\":\"Levitus,
        Sydney; Antonov, John I.; Boyer, Tim P.; Baranova, Olga K.; Garc\xEDa, Hern\xE1n
        E.; Locarnini, Ricardo A.; Mishonov, Alexey V.; Reagan, James R.; [Seidov,
        Dan; Yarosh, Evgeney; Zweng, Melissa M. (2017). NCEI ocean heat content, temperature
        anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric
        sea level anomalies, and total steric sea level anomalies from 1955 to present
        calculated from in situ oceanographic subsurface profile data (NCEI Accession
        0164586). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v53f4mvp.\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"noaa-cdr\",\"msft:container\":\"ocean-heat-content\",\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"noaacdr\",\"msft:short_description\":\"The
        Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content
        anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal
        (five-yearly) scales.\"},{\"id\":\"cil-gdpcir-cc0\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0\"},{\"rel\":\"license\",\"href\":\"https://spdx.org/licenses/CC0-1.0.html\",\"type\":\"text/html\",\"title\":\"Creative
        Commons Zero v1.0 Universal\"},{\"rel\":\"cite-as\",\"href\":\"https://zenodo.org/record/6403794\",\"type\":\"text/html\"},{\"rel\":\"describedby\",\"href\":\"https://github.com/ClimateImpactLab/downscaleCMIP6/\",\"type\":\"text/html\",\"title\":\"Project
        homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL
        Global Downscaled Projections for Climate Impacts Research (CC0-1.0)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"1950-01-01T00:00:00Z\",\"2100-12-31T00:00:00Z\"]]}},\"license\":\"CC0-1.0\",\"sci:doi\":\"10.5194/egusphere-2022-1513\",\"keywords\":[\"CMIP6\",\"Climate
        Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate
        Impact Lab\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"cmip6:variable\":[\"pr\",\"tasmax\",\"tasmin\"],\"cmip6:source_id\":[\"FGOALS-g3\",\"INM-CM4-8\",\"INM-CM5-0\"],\"cmip6:experiment_id\":[\"historical\",\"ssp126\",\"ssp245\",\"ssp370\",\"ssp585\"],\"cmip6:institution_id\":[\"BCC\",\"CAS\",\"CCCma\",\"CMCC\",\"CSIRO\",\"CSIRO-ARCCSS\",\"DKRZ\",\"EC-Earth-Consortium\",\"INM\",\"MIROC\",\"MOHC\",\"MPI-M\",\"NCC\",\"NOAA-GFDL\",\"NUIST\"]},\"description\":\"The
        World Climate Research Programme's [6th Coupled Model Intercomparison Project
        (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an
        enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe
        [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)
        dataset makes this modeling more applicable to understanding the impacts of
        changes in the climate on humans and society with two key developments: trend-preserving
        bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)
        provides global, daily minimum and maximum air temperature at the surface
        (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)
        corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5
        scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n##
        Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary
        Computer. The dataset is made of of three collections, distinguished by data
        license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n*
        [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach
        modeling center with bias corrected and downscaled data in this collection
        falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection)
        to see which model is in each collection, and see the section below on [Citing,
        Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project)
        for citations and additional information about each license.\\n\\n## Data
        format & contents\\n\\nThe data is stored as partitioned zarr stores (see
        [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which
        includes thousands of data and metadata files covering the full time span
        of the experiment. Historical zarr stores contain just over 50 GB, while SSP
        zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with
        dimensions time (daily datetime), lat (float latitude), and lon (float longitude).
        The data is chunked at each interval of 365 days and 90 degree interval of
        latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each
        chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily,
        excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,
        excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,
        depending on data availability in the source GCM.\\n\\nThe spatial domain
        covers all 0.25-degree grid cells, indexed by the grid center, with grid edges
        on the quarter-degree, using a -180 to 180 longitude convention. Thus, the
        \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D
        coordinate extends from -89.875 to 89.875, with intermediate values at each
        0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n##
        Available institutions, models, and scenarios by license collection\\n\\n|
        Modeling institution |   Source model    |           Available experiments
        \           |   License collection   |\\n| -------------------- | -----------------
        | ------------------------------------------ | ---------------------- |\\n|
        CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5
        \          | Public domain datasets |\\n| INM                  | INM-CM4-8
        \        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets
        |\\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0,
        and SSP5-8.5 | Public domain datasets |\\n| BCC                  | BCC-CSM2-MR
        \      | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n|
        CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5
        \    | CC-BY-40               |\\n| CMCC                 | CMCC-ESM2         |
        ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\\n|
        CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      |
        CC-BY-40               |\\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6,
        SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\\n| MIROC                |
        MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| MIROC                | MIROC6            | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| MOHC                 |
        HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40
        \              |\\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| MPI-M                |
        MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and
        SSP5-8.5                      | CC-BY-40               |\\n| NCC                  |
        NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| NCC                  | NorESM2-MM        | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| NOAA-GFDL
        \           | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      |
        CC-BY-40               |\\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| NUIST                |
        NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6,
        ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\\n| EC-Earth-Consortium
        \ | EC-Earth3-AerChem | ssp370                                     | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and
        ssp585                          | CC-BY-40               |\\n| EC-Earth-Consortium
        \ | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6,
        ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\\n| CCCma                |
        CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]
        \          |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6
        precipitation data was available. Therefore, we provide `tasmin` and `tamax`
        for this model and experiment, but not `pr`. All other model/experiment combinations
        in the above table include all three variables.\\n\\n[^2]: The institution
        which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while
        the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution
        component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`
        scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),
        but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
        in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical
        bias correction and downscaling algorithms, which are specifically designed
        to accurately represent changes in the extremes. For this reason, we selected
        Quantile Delta Mapping (QDM), following the method introduced by [Cannon et
        al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific
        trends from the GCM while fitting the full distribution for a given day-of-year
        to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored
        to increase spatial resolution while preserving extreme behavior, Quantile-Preserving
        Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide
        a robust means to handle both the central and tail behavior seen in climate
        model output, while aligning the full distribution to a state-of-the-art reanalysis
        dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor
        further documentation, see [Global downscaled projections for climate impacts
        research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)
        (EGUsphere, 2022 [preprint]).\\n\\n\\n## Citing, licensing, and using data
        produced by this project\\n\\nProjects making use of the data produced as
        part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts
        Research (CIL GDPCIR) project are requested to cite both this project and
        the source datasets from which these results are derived. Additionally, the
        use of data derived from some GCMs *requires* citations, and some modeling
        centers impose licensing restrictions & requirements on derived works. See
        each GCM's license info in the links below for more information.\\n\\n###
        CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works.
        Our method documentation paper may be cited using the following:\\n\\n> Gergel,
        D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish,
        M. A., and Kopp, R. E.: Global downscaled projections for climate impacts
        research (GDPCIR): preserving extremes for modeling future climate impacts,
        EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe
        code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly
        McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado
        (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n###
        ERA5\\n\\nAdditionally, we request you cite the historical dataset used in
        bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset
        on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\n\\n>
        Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:
        1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n>
        Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.
        Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed
        on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n>
        Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus
        Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,
        2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n###
        GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available
        through the Earth System Grid Federation (ESGF) are subject to Creative Commons
        [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA
        4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate
        Impact Lab has reached out to each of the modeling institutions to request
        waivers from these terms so the outputs of this project may be used with fewer
        restrictions, and has been granted permission to release the data using the
        licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following
        bias corrected and downscaled model simulations are available in the public
        domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\n\\n*
        **FGOALS-g3**\\n\\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output
        prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6
        ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0
        version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version
        20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\n\\n\\n*
        **INM-CM4-8**\\n\\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8
        model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\n\\n
        \ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5
        version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n####
        CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations
        are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
        Note that this license requires citation of the source model output (included
        here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\n\\n*
        **ACCESS-CM2**\\n\\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,
        Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,
        Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;
        Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,
        Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,
        Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,
        Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;
        Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2
        model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,
        Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,
        Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;
        Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,
        Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,
        Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,
        Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;
        Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output
        prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\n\\n
        \ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;
        Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;
        Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,
        Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5
        model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,
        Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;
        Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,
        Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared
        for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\n\\n\\n*
        **BCC-CSM2-MR**\\n\\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;
        Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;
        Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.
        Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,
        Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,
        Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;
        SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\n\\n\\n*
        **CMCC-CM2-SR5**\\n\\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC
        CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5
        version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\n\\n\\n*
        **CMCC-ESM2**\\n\\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme
        **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version
        20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n,
        Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;
        SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\n\\n\\n*
        **EC-Earth3-AerChem**\\n\\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.
        Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\n\\n\\n*
        **EC-Earth3-CC**\\n\\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium
        EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\n\\n\\n*
        **EC-Earth3-Veg-LR**\\n\\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;
        SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\n\\n\\n*
        **EC-Earth3-Veg**\\n\\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\n\\n\\n*
        **EC-Earth3**\\n\\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\n\\n\\n*
        **GFDL-CM4**\\n\\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,
        Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,
        Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,
        Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen
        M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,
        Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;
        Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;
        Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;
        Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,
        P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.
        *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;
        Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,
        Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;
        Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,
        Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,
        Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;
        Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;
        Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,
        Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4
        model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n
        \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris;
        McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;
        Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,
        Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;
        Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,
        David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,
        Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,
        John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry
        W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;
        Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4
        model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;
        Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,
        John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,
        Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL
        GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\n\\n\\n*
        **HadGEM3-GC31-LL**\\n\\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;
        Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output
        prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for
        CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;
        SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\n\\n\\n*
        **MIROC-ES2L**\\n\\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,
        Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,
        Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,
        Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,
        Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.
        Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;
        Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;
        Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,
        Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,
        Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.
        Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\n\\n\\n*
        **MIROC6**\\n\\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.
        *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.
        *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\n\\n\\n*
        **MPI-ESM1-2-HR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;
        Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,
        Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,
        J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;
        Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,
        Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;
        Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Schupfner, Martin; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;
        Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,
        Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;
        Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg;
        Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,
        Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;
        Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;
        Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang;
        Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,
        Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;
        Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich
        **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.
        Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\n\\n\\n*
        **MPI-ESM1-2-LR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,
        Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;
        Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de
        Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;
        Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,
        Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;
        Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,
        Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco;
        Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,
        Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;
        von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin;
        Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,
        Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,
        Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,
        Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von
        Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,
        Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;
        Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR
        model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n
        \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model
        output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\n\\n\\n*
        **NorESM2-LM**\\n\\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk
        Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model
        output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\n\\n\\n*
        **NorESM2-MM**\\n\\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland,
        \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model
        output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\n\\n\\n*
        **UKESM1-0-LL**\\n\\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,
        Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.
        *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Good, Peter; Sellar, Alistair; Tang, Yongming;
        Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy
        **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;
        SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n\\n*
        **CanESM5**\\n\\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).
        Note: this dataset was previously licensed\\n  under CC BY-SA 4.0, but was
        relicensed as CC BY 4.0 in March, 2023.\\n\\n  CMIP Citation:\\n\\n  > Swart,
        Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,
        John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James
        R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,
        Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;
        Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model
        output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,
        Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;
        Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,
        Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von
        Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma
        CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis
        work is the result of many years worth of work by members of the [Climate
        Impact Lab](https://impactlab.org), but would not have been possible without
        many contributions from across the wider scientific and computing communities.\\n\\nSpecifically,
        we would like to acknowledge the World Climate Research Programme's Working
        Group on Coupled Modeling, which is responsible for CMIP, and we would like
        to thank the climate modeling groups for producing and making their model
        output available. We would particularly like to thank the modeling institutions
        whose results are included as an input to this repository (listed above) for
        their contributions to the CMIP6 project and for responding to and granting
        our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty
        Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)
        (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))
        and Google Cloud and the Google Public Datasets program for making the [CMIP6
        Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)
        possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),
        [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),
        [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),
        and [Charles Stern](https://github.com/cisaacstern) for the huge amount of
        work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,
        analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the
        [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)
        ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in
        particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),
        and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM
        bias correction method in the xclim python package, supporting our QPLAD implementation
        into the package, and ongoing support in integrating dask into downscaling
        workflows. For method advice and useful conversations, we would like to thank
        Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n##
        Financial support\\n\\nThis research has been supported by The Rockefeller
        Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n*
        CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n*
        Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate
        Impact Lab homepage: [impactlab.org](https://impactlab.org)\",\"item_assets\":{\"pr\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Precipitation\",\"description\":\"Precipitation\"},\"tasmax\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily
        Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface
        Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily
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        Commons Attribution 4.0 International\"},{\"rel\":\"cite-as\",\"href\":\"https://zenodo.org/record/6403794\",\"type\":\"text/html\"},{\"rel\":\"describedby\",\"href\":\"https://github.com/ClimateImpactLab/downscaleCMIP6/\",\"type\":\"text/html\",\"title\":\"Project
        homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL
        Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/cil-gdpcir-cc-by.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"1950-01-01T00:00:00Z\",\"2100-12-31T00:00:00Z\"]]}},\"license\":\"CC-BY-4.0\",\"sci:doi\":\"10.5194/egusphere-2022-1513\",\"keywords\":[\"CMIP6\",\"Climate
        Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate
        Impact Lab\",\"roles\":[\"producer\"]},{\"url\":\"https://planetarycomputer.microsoft.com/\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"cmip6:variable\":[\"pr\",\"tasmax\",\"tasmin\"],\"cmip6:source_id\":[\"BCC-CSM2-MR\",\"ACCESS-ESM1-5\",\"ACCESS-CM2\",\"MIROC-ES2L\",\"MIROC6\",\"NorESM2-LM\",\"NorESM2-MM\",\"GFDL-CM4\",\"GFDL-ESM4\",\"NESM3\",\"MPI-ESM1-2-HR\",\"HadGEM3-GC31-LL\",\"UKESM1-0-LL\",\"MPI-ESM1-2-LR\",\"EC-Earth3\",\"EC-Earth3-AerChem\",\"EC-Earth3-CC\",\"EC-Earth3-Veg\",\"EC-Earth3-Veg-LR\",\"CMCC-CM2-SR5\",\"CMCC-ESM2\"],\"cmip6:experiment_id\":[\"historical\",\"ssp126\",\"ssp245\",\"ssp370\",\"ssp585\"],\"cmip6:institution_id\":[\"BCC\",\"CAS\",\"CCCma\",\"CMCC\",\"CSIRO\",\"CSIRO-ARCCSS\",\"DKRZ\",\"EC-Earth-Consortium\",\"INM\",\"MIROC\",\"MOHC\",\"MPI-M\",\"NCC\",\"NOAA-GFDL\",\"NUIST\"]},\"description\":\"The
        World Climate Research Programme's [6th Coupled Model Intercomparison Project
        (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an
        enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe
        [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)
        dataset makes this modeling more applicable to understanding the impacts of
        changes in the climate on humans and society with two key developments: trend-preserving
        bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)
        provides global, daily minimum and maximum air temperature at the surface
        (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)
        corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5
        scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n##
        Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary
        Computer. The dataset is made of of three collections, distinguished by data
        license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n*
        [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach
        modeling center with bias corrected and downscaled data in this collection
        falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection)
        to see which model is in each collection, and see the section below on [Citing,
        Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project)
        for citations and additional information about each license.\\n\\n## Data
        format & contents\\n\\nThe data is stored as partitioned zarr stores (see
        [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which
        includes thousands of data and metadata files covering the full time span
        of the experiment. Historical zarr stores contain just over 50 GB, while SSP
        zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with
        dimensions time (daily datetime), lat (float latitude), and lon (float longitude).
        The data is chunked at each interval of 365 days and 90 degree interval of
        latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each
        chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily,
        excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,
        excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,
        depending on data availability in the source GCM.\\n\\nThe spatial domain
        covers all 0.25-degree grid cells, indexed by the grid center, with grid edges
        on the quarter-degree, using a -180 to 180 longitude convention. Thus, the
        \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D
        coordinate extends from -89.875 to 89.875, with intermediate values at each
        0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n##
        Available institutions, models, and scenarios by license collection\\n\\n|
        Modeling institution |   Source model    |           Available experiments
        \           |   License collection   |\\n| -------------------- | -----------------
        | ------------------------------------------ | ---------------------- |\\n|
        CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5
        \          | Public domain datasets |\\n| INM                  | INM-CM4-8
        \        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets
        |\\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0,
        and SSP5-8.5 | Public domain datasets |\\n| BCC                  | BCC-CSM2-MR
        \      | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n|
        CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5
        \    | CC-BY-40               |\\n| CMCC                 | CMCC-ESM2         |
        ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\\n|
        CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      |
        CC-BY-40               |\\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6,
        SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\\n| MIROC                |
        MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| MIROC                | MIROC6            | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| MOHC                 |
        HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40
        \              |\\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| MPI-M                |
        MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and
        SSP5-8.5                      | CC-BY-40               |\\n| NCC                  |
        NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40
        \              |\\n| NCC                  | NorESM2-MM        | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| NOAA-GFDL
        \           | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      |
        CC-BY-40               |\\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6,
        SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\\n| NUIST                |
        NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6,
        ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\\n| EC-Earth-Consortium
        \ | EC-Earth3-AerChem | ssp370                                     | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and
        ssp585                          | CC-BY-40               |\\n| EC-Earth-Consortium
        \ | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40
        \              |\\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6,
        ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\\n| CCCma                |
        CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]
        \          |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6
        precipitation data was available. Therefore, we provide `tasmin` and `tamax`
        for this model and experiment, but not `pr`. All other model/experiment combinations
        in the above table include all three variables.\\n\\n[^2]: The institution
        which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while
        the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution
        component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`
        scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),
        but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
        in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical
        bias correction and downscaling algorithms, which are specifically designed
        to accurately represent changes in the extremes. For this reason, we selected
        Quantile Delta Mapping (QDM), following the method introduced by [Cannon et
        al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific
        trends from the GCM while fitting the full distribution for a given day-of-year
        to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored
        to increase spatial resolution while preserving extreme behavior, Quantile-Preserving
        Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide
        a robust means to handle both the central and tail behavior seen in climate
        model output, while aligning the full distribution to a state-of-the-art reanalysis
        dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor
        further documentation, see [Global downscaled projections for climate impacts
        research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)
        (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced
        by this project\\n\\nProjects making use of the data produced as part of the
        Climate Impact Lab Global Downscaled Projections for Climate Impacts Research
        (CIL GDPCIR) project are requested to cite both this project and the source
        datasets from which these results are derived. Additionally, the use of data
        derived from some GCMs *requires* citations, and some modeling centers impose
        licensing restrictions & requirements on derived works. See each GCM's license
        info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers
        are requested to cite this project in derived works. Our method documentation
        paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S.
        B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,
        R. E.: Global downscaled projections for climate impacts research (GDPCIR):
        preserving extremes for modeling future climate impacts, EGUsphere [preprint],
        https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository
        may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster
        Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:
        (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally,
        we request you cite the historical dataset used in bias correction and downscaling,
        ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\n\\n>
        Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:
        1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n>
        Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.
        Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed
        on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n>
        Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus
        Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,
        2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n###
        GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available
        through the Earth System Grid Federation (ESGF) are subject to Creative Commons
        [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA
        4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate
        Impact Lab has reached out to each of the modeling institutions to request
        waivers from these terms so the outputs of this project may be used with fewer
        restrictions, and has been granted permission to release the data using the
        licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following
        bias corrected and downscaled model simulations are available in the public
        domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\n\\n*
        **FGOALS-g3**\\n\\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output
        prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6
        ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0
        version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version
        20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\n\\n\\n*
        **INM-CM4-8**\\n\\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8
        model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\n\\n
        \ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;
        Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,
        Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.
        *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,
        Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;
        Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5
        version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n####
        CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations
        are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
        Note that this license requires citation of the source model output (included
        here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.
        Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\n\\n*
        **ACCESS-CM2**\\n\\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,
        Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,
        Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;
        Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,
        Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,
        Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,
        Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;
        Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2
        model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,
        Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,
        Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;
        Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,
        Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,
        Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,
        Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;
        Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output
        prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\n\\n
        \ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;
        Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;
        Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,
        Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5
        model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,
        Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;
        Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,
        Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared
        for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\n\\n\\n*
        **BCC-CSM2-MR**\\n\\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;
        Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;
        Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.
        Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,
        Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,
        Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;
        SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\n\\n\\n*
        **CMCC-CM2-SR5**\\n\\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC
        CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5
        model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5
        version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\n\\n\\n*
        **CMCC-ESM2**\\n\\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme
        **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version
        20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Lovato, Tomas; Peano, Daniele; Butensch\xF6n,
        Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;
        SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\n\\n\\n*
        **EC-Earth3-AerChem**\\n\\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.
        Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\n\\n\\n*
        **EC-Earth3-CC**\\n\\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium
        EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\n\\n\\n*
        **EC-Earth3-Veg-LR**\\n\\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium
        EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2020)**.
        *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;
        SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\n\\n\\n*
        **EC-Earth3-Veg**\\n\\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\n\\n\\n*
        **EC-Earth3**\\n\\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n  ScenarioMIP
        Citation:\\n\\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium
        EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\n\\n\\n*
        **GFDL-CM4**\\n\\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,
        Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,
        Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,
        Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen
        M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,
        Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;
        Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;
        Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;
        Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,
        P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.
        *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;
        Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,
        Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;
        Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,
        Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,
        Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;
        Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;
        Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,
        Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4
        model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n
        \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris;
        McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;
        Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;
        Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,
        Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;
        Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,
        David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,
        Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,
        John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry
        W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;
        Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4
        model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid
        Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;
        Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,
        John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;
        Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,
        Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL
        GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\n\\n\\n*
        **HadGEM3-GC31-LL**\\n\\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;
        Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output
        prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for
        CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;
        SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\n\\n\\n*
        **MIROC-ES2L**\\n\\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,
        Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,
        Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,
        Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,
        Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.
        Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;
        Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;
        Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,
        Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,
        Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.
        Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\n\\n\\n*
        **MIROC6**\\n\\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.
        *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n  ScenarioMIP
        Citation:\\n\\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.
        *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\n\\n\\n*
        **MPI-ESM1-2-HR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;
        Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,
        Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,
        J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;
        Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,
        Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;
        Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Schupfner, Martin; Wieners, Karl-Hermann;
        Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;
        Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,
        Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;
        Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg;
        Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,
        Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;
        Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;
        Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang;
        Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,
        Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;
        Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich
        **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.
        Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\n\\n\\n*
        **MPI-ESM1-2-LR**\\n\\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,
        Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;
        Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de
        Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;
        Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,
        Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;
        Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,
        Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;
        M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;
        Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,
        Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,
        Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared
        for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Wieners, Karl-Hermann; Giorgetta, Marco;
        Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,
        Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;
        von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin;
        Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,
        Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,
        Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,
        Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von
        Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,
        Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;
        Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR
        model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System
        Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n
        \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model
        output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\n\\n\\n*
        **NorESM2-LM**\\n\\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk
        Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model
        output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\n\\n\\n*
        **NorESM2-MM**\\n\\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland,
        \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,
        Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger,
        J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;
        Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger
        Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;
        Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,
        Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model
        output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas;
        Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok
        Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry;
        Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,
        Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,
        Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,
        Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,
        Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.
        Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\n\\n\\n*
        **UKESM1-0-LL**\\n\\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\n\\n
        \ CMIP Citation:\\n\\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,
        Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.
        *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.
        Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n
        \ ScenarioMIP Citation:\\n\\n  > Good, Peter; Sellar, Alistair; Tang, Yongming;
        Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy
        **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.
        SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;
        SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n*
        **CanESM5**\\n\\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).
        Note: this dataset was previously licensed\\n  under CC BY-SA 4.0, but was
        relicensed as CC BY 4.0 in March, 2023.\\n\\n  CMIP Citation:\\n\\n  > Swart,
        Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,
        John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James
        R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,
        Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;
        Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model
        output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.
        https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n  ScenarioMIP Citation:\\n\\n
        \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,
        Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;
        Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,
        Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von
        Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma
        CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth
        System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis
        work is the result of many years worth of work by members of the [Climate
        Impact Lab](https://impactlab.org), but would not have been possible without
        many contributions from across the wider scientific and computing communities.\\n\\nSpecifically,
        we would like to acknowledge the World Climate Research Programme's Working
        Group on Coupled Modeling, which is responsible for CMIP, and we would like
        to thank the climate modeling groups for producing and making their model
        output available. We would particularly like to thank the modeling institutions
        whose results are included as an input to this repository (listed above) for
        their contributions to the CMIP6 project and for responding to and granting
        our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty
        Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)
        (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))
        and Google Cloud and the Google Public Datasets program for making the [CMIP6
        Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)
        possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),
        [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),
        [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),
        and [Charles Stern](https://github.com/cisaacstern) for the huge amount of
        work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,
        analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the
        [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)
        ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in
        particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),
        and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM
        bias correction method in the xclim python package, supporting our QPLAD implementation
        into the package, and ongoing support in integrating dask into downscaling
        workflows. For method advice and useful conversations, we would like to thank
        Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n##
        Financial support\\n\\nThis research has been supported by The Rockefeller
        Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n*
        CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n*
        Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate
        Impact Lab homepage: [impactlab.org](https://impactlab.org)\",\"item_assets\":{\"pr\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Precipitation\",\"description\":\"Precipitation\"},\"tasmax\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily
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        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2018-04-30T00:18:50Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"ESA\",\"Copernicus\",\"Sentinel\",\"Air
        Quality\",\"Climate Change\",\"Forecasting\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"platform\":[\"Sentinel
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        Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p)
        mission provides high spatio-temporal resolution measurements of the Earth's
        atmosphere. The mission consists of one satellite carrying the [TROPOspheric
        Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies
        in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html)
        spacecraft, allowing utilization of co-located cloud mask data provided by
        the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging)
        (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane
        product.\\n\\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric
        data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat)
        and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions
        and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5)
        mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products)
        provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide
        and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone
        and cloud & aerosol information. These measurements are used for improving
        air quality forecasts and monitoring the concentrations of atmospheric constituents.\\n\\nThis
        STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format,
        since April 2018 for the following products:\\n\\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index):
        Ultraviolet aerosol index\\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height):
        Aerosol layer height\\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane):
        Methane (CH<sub>4</sub>) total column\\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud):
        Cloud fraction, albedo, and top pressure\\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide):
        Carbon monoxide (CO) total column\\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde):
        Formaldehyde (HCHO) total column\\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide):
        Nitrogen dioxide (NO<sub>2</sub>) total column\\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column):
        Ozone (O<sub>3</sub>) total column\\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column):
        Ozone (O<sub>3</sub>) tropospheric column\\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide):
        Sulfur dioxide (SO<sub>2</sub>) total column\\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary):
        Cloud from the Suomi NPP mission, band 3\\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary):
        Cloud from the Suomi NPP mission, band 6\\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary):
        Cloud from the Suomi NPP mission, band 7\\n\",\"item_assets\":{\"co\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Carbon
        Monoxide Total Column\"},\"o3\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Ozone
        Total Column\"},\"ch4\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Methane
        Total Column\"},\"no2\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Nitrogen
        Dioxide Total Column\"},\"so2\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Sulphur
        Dioxide Total Column\"},\"hcho\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Formaldehyde
        Total Column\"},\"cloud\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Cloud
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        Layer Height\"},\"np-bd3\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"VIIRS/NPP
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        Band 6 Cloud Mask\"},\"np-bd7\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"VIIRS/NPP
        Band 7 Cloud Mask\"},\"o3-tcl\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"title\":\"Ozone
        Tropospheric Column\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"sentinel-5p\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"sentinel5euwest\",\"msft:short_description\":\"Sentinel-5P
        Level 2 atmospheric monitoring products in NetCDF format\"},{\"id\":\"sentinel-3-olci-wfr-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-wfr-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-wfr-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Water (LRR and LFR) Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-olci-wfr-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Water (Full Resolution)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-olci-wfr-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
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        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2017-11-01T00:07:01.738487Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"ESA\",\"Copernicus\",\"Sentinel\",\"Water\",\"Ocean\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"olci-wfr\"],\"s3:product_type\":[\"OL_2_WFR___\"],\"sat:orbit_state\":[\"descending\",\"ascending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2]
        products containing data on water-leaving reflectance, ocean color, and more.\\n\\n##
        Data files\\n\\nThis dataset includes data on:\\n\\n- Surface directional
        reflectance\\n- Chlorophyll-a concentration\\n- Suspended matter concentration\\n-
        Energy flux\\n- Aerosol load\\n- Integrated water vapor column\\n\\nEach variable
        is contained within NetCDF files. Error estimates are available for each product.\\n\\n##
        Processing overview\\n\\nThe values in the data files have been converted
        from Top of Atmosphere radiance to reflectance, and include various corrections
        for gaseous absorption and pixel classification. More information about the
        product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from November 2017 to present.\\n\\n[olci-l2]:
        https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\\n\",\"item_assets\":{\"iwv\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa18\",\"description\":\"Band
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        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01}],\"description\":\"Integrated
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        active radiation\"},\"trsp\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01}],\"description\":\"Transparency
        properties of water\"},\"wqsf\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Water
        quality and science flags\"},\"w-aer\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Aerosol
        over water\"},\"chl-nn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Neural
        net chlorophyll concentration\"},\"iop-nn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Inherent
        optical properties of water\"},\"tsm-nn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Total
        suspended matter concentration\"},\"chl-oc4me\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01}],\"description\":\"OC4Me
        algorithm chlorophyll concentration\"},\"tie-meteo\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Tie-point
        meteo annotations\"},\"browse-jpg\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"description\":\"Preview
        image produced by the European Organisation for the Exploitation of Meteorological
        Satellites (EUMETSAT)\"},\"eop-metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"Metadata
        produced by the European Organisation for the Exploitation of Meteorological
        Satellites (EUMETSAT)\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"tie-geometries\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Tie-point
        geometry annotations\"},\"geo-coordinates\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Geo
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        sea level anomalies, and total steric sea level anomalies from 1955 to present
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        Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1)
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        parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF
        file for all regions over land and ocean free of snow/ice cover, excluding
        high cloud fraction data. The retrieved and derived aerosol parameters are:\\n\\n-
        Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\\n- Error
        estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and
        2250 nm\\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and
        2250 nm\\n- Fine-mode AOD at 550nm\\n- Aerosol Angstrom parameter between
        550 and 865nm\\n- Dust AOD at 550nm\\n- Aerosol absorption optical depth at
        550nm\\n\\nAtmospherically corrected nadir surface directional reflectances
        at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution
        are also provided. More information about the product and data processing
        can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from April 2020 to present.\\n\",\"item_assets\":{\"ntc-aod\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN_440\",\"description\":\"OLCI
        channel Oa03\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"SYN_550\",\"description\":\"SLSTR
        nadir and oblique channel S1\",\"center_wavelength\":0.55,\"full_width_half_max\":0.02},{\"name\":\"SYN_670\",\"description\":\"SLSTR
        nadir and oblique channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN_865\",\"description\":\"OLCI
        channel Oa17, SLSTR nadir and oblique channel S2\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN_1600\",\"description\":\"SLSTR
        nadir and oblique channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN_2250\",\"description\":\"SLSTR
        nadir and oblique channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05}],\"description\":\"Global
        aerosol parameters\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        global aerosol and surface reflectance at super-pixel (4.5km) resolution (SYNERGY
        AOD).\"},{\"id\":\"sentinel-3-synergy-v10-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-v10-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-v10-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        10-Day Surface Reflectance and NDVI Product (SPOT VEGETATION) User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-v10-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        10-Day Surface Reflectance and NDVI (SPOT VEGETATION)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-v10-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        10-Day Surface Reflectance and NDVI (SPOT VEGETATION) Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-v10-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-56.0,180.0,75.0]]},\"temporal\":{\"interval\":[[\"2018-09-27T11:17:21Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Reflectance\",\"NDVI\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
        channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR
        nadir and oblique channels S5, S6\",\"center_wavelength\":1.665,\"full_width_half_max\":0.085}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\",\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"synergy-v10\"],\"s3:product_type\":[\"SY_2_V10___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance
        and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)
        products, which are SPOT VEGETATION Continuity Products similar to those obtained
        from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments)
        onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum
        Normalized Difference Vegetation Index (NDVI) composite, which is derived
        from ground reflectance during a 10-day window, and four surface reflectance
        bands:\\n\\n- B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n- B3 (NIR, 835nm)\\n-
        MIR (SWIR, 1665nm)\\n\\nThe four reflectance bands have center wavelengths
        matching those on the original SPOT VEGETATION instrument. The NDVI variable,
        which is an indicator of the amount of vegetation, is derived from the B3
        and B2 bands.\\n\\n## Data files\\n\\nThe four reflectance bands and NDVI
        values are each contained in dedicated NetCDF files. Additional metadata are
        delivered in annotation NetCDF files, each containing a single variable, including
        the geometric viewing and illumination conditions, the total water vapour
        and ozone columns, and the aerosol optical depth.\\n\\nEach 10-day product
        is delivered as a set of 10 rectangular scenes:\\n\\n- AFRICA\\n- NORTH_AMERICA\\n-
        SOUTH_AMERICA\\n- CENTRAL_AMERICA\\n- NORTH_ASIA\\n- WEST_ASIA\\n- SOUTH_EAST_ASIA\\n-
        ASIAN_ISLANDS\\n- AUSTRALASIA\\n- EUROPE\\n\\nMore information about the product
        and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from September 2018 to present.\\n\",\"item_assets\":{\"ag\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Aerosol
        optical thickness data\"},\"b0\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
        channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02}],\"description\":\"Surface
        Reflectance Data Set associated with VGT-B0 channel\"},\"b2\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035}],\"description\":\"Surface
        Reflectance Data Set associated with VGT-B2 channel\"},\"b3\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055}],\"description\":\"Surface
        Reflectance Data Set associated with VGT-B3 channel\"},\"og\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Total
        Ozone column data\"},\"sm\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Status
        Map data\"},\"tg\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Synthesis
        time data\"},\"mir\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"MIR\",\"description\":\"SLSTR
        nadir and oblique channels S5, S6\",\"center_wavelength\":1.665,\"full_width_half_max\":0.085}],\"description\":\"Surface
        Reflectance Data Set associated with VGT-MIR channel\"},\"saa\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Solar
        azimuth angle data\"},\"sza\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Solar
        zenith angle data\"},\"vaa\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"View
        azimuth angle data\"},\"vza\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"View
        zenith angle data\"},\"wvg\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Total
        column Water vapour data\"},\"ndvi\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055}],\"description\":\"Normalised
        difference vegetation index\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        10-day surface reflectance and NDVI (SYNERGY V10, a SPOT VEGETATION Continuity
        Product).\"},{\"id\":\"sentinel-3-olci-lfr-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-lfr-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-lfr-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Land (LRR and LFR) Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-olci-lfr-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Land (Full Resolution)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-olci-lfr-l2-netcdf-thumb.png\",\"title\":\"Sentinel-5P
        Level-2 NetCDF Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-olci-lfr-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2016-04-25T11:33:47.368562Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"ESA\",\"Copernicus\",\"Sentinel\",\"Land\",\"Biomass\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"olci-lfr\"],\"s3:product_type\":[\"OL_2_LFR___\"],\"sat:orbit_state\":[\"descending\",\"ascending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2]
        products containing data on global vegetation, chlorophyll, and water vapor.\\n\\n##
        Data files\\n\\nThis dataset includes data on three primary variables:\\n\\n*
        OLCI global vegetation index file\\n* terrestrial Chlorophyll index file\\n*
        integrated water vapor over water file.\\n\\nEach variable is contained within
        a separate NetCDF file, and is cataloged as an asset in each Item.\\n\\nSeveral
        associated variables are also provided in the annotations data files:\\n\\n*
        rectified reflectance for red and NIR channels (RC681 and RC865)\\n* classification,
        quality and science flags (LQSF)\\n* common data such as the ortho-geolocation
        of land pixels, solar and satellite angles, atmospheric and meteorological
        data, time stamp or instrument information. These variables are inherited
        from Level-1B products.\\n\\nThis full resolution product offers a spatial
        sampling of approximately 300 m.\\n\\n## Processing overview\\n\\nThe values
        in the data files have been converted from Top of Atmosphere radiance to reflectance,
        and include various corrections for gaseous absorption and pixel classification.
        More information about the product and data processing can be found in the
        [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from April 2016 to present.\\n\\n[olci-l2]:
        https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\\n\",\"item_assets\":{\"iwv\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01}],\"description\":\"Integrated
        water vapour column\"},\"lqsf\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Land
        quality and science flags\"},\"ogvi\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"OLCI
        Global Vegetation Index (OGVI)\"},\"otci\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075}],\"description\":\"OLCI
        Terrestrial Chlorophyll Index (OTCI)\"},\"gifapar\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Green
        instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)\"},\"rc-ogvi\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Rectified
        reflectance\"},\"tie-meteo\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Tie-point
        meteo annotations\"},\"rc-gifapar\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Rectified
        reflectance\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"tie-geometries\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Tie-point
        geometry annotations\"},\"geo-coordinates\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Geo
        coordinate annotations\"},\"instrument-data\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Instrument
        annotations\"},\"time-coordinates\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time
        coordinate annotations\"},\"tie-geo-coordinates\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Tie-point
        geo coordinate annotations\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        Land Full Resolution vegetation index, chlorophyll index, and water vapor
        (OLCI LFR).\"},{\"id\":\"sentinel-3-sral-lan-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-lan-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-lan-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Land Radar Altimetry Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-lan-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Land Radar Altimetry\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-sral-lan-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Land Radar Altimetry Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-sral-lan-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-81.5,180,81.5]]},\"temporal\":{\"interval\":[[\"2016-03-01T14:07:51.632846Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Radar\",\"Altimetry\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"SRAL\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"sral-lan\"],\"s3:product_type\":[\"SR_2_LAN___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products)
        products, which contain data on land radar altimetry measurements. Each product
        contains three NetCDF files:\\n\\n- A reduced data file containing a subset
        of the 1 Hz Ku-band parameters.\\n- A standard data file containing the standard
        1 Hz and 20 Hz Ku- and C-band parameters.\\n- An enhanced data file containing
        the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms
        and parameters necessary to reprocess the data.\\n\\nMore information about
        the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from March 2016 to present.\\n\",\"item_assets\":{\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"reduced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Reduced
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"enhanced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Enhanced
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band
        C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"standard-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Standard
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band
        C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        radar altimetry over land (SRAL LAN).\"},{\"id\":\"sentinel-3-slstr-lst-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-lst-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-lst-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Land Surface Temperature Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-lst-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Land Surface Temperature\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-lst-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Land Surface Temperature Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-slstr-lst-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2016-04-19T01:35:17.188500Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Temperature\",\"Land\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"S8\",\"description\":\"Band
        8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band
        9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"slstr-lst\"],\"s3:product_type\":[\"SL_2_LST___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst)
        products containing data on land surface temperature measurements on a 1km
        grid. Radiance is measured in two channels to determine the temperature of
        the Earth's surface skin in the instrument field of view, where the term \\\"skin\\\"
        refers to the top surface of bare soil or the effective emitting temperature
        of vegetation canopies as viewed from above.\\n\\n## Data files\\n\\nThe dataset
        includes data on the primary measurement variable, land surface temperature,
        in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains
        several ancillary variables:\\n\\n- Normalized Difference Vegetation Index\\n-
        Surface biome classification\\n- Fractional vegetation cover\\n- Total water
        vapor column\\n\\nIn addition to the primary and ancillary data files, a standard
        set of annotation data files provide meteorological information, geolocation
        and time coordinates, geometry information, and quality flags. More information
        about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from April 2016 to present.\\n\\n##
        STAC Item geometries\\n\\nThe Collection contains small \\\"chips\\\" and
        long \\\"stripes\\\" of data collected along the satellite direction of travel.
        Approximately five percent of the STAC Items describing long stripes of data
        contain geometries that encompass a larger area than an exact concave hull
        of the data extents. This may require additional filtering when searching
        the Collection for Items that spatially intersect an area of interest.\\n\",\"item_assets\":{\"lst-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S8\",\"description\":\"Band
        8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band
        9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"description\":\"Land
        Surface Temperature (LST) values\"},\"slstr-met-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Meteorological
        parameters regridded onto the 16km tie points\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"slstr-time-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time
        annotations for the 1km grid\"},\"slstr-flags-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Global
        flags for the 1km TIR grid, nadir view\"},\"lst-ancillary-ds\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"LST
        ancillary measurement dataset\"},\"slstr-indices-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Scan,
        pixel and detector indices annotations for the 1km TIR grid, nadir view\"},\"slstr-geodetic-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution geodetic coordinates for the 1km TIR grid, nadir view\"},\"slstr-geodetic-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        geodetic coordinates\"},\"slstr-geometry-tn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        solar and satellite geometry annotations, nadir view\"},\"slstr-cartesian-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution cartesian coordinates for the 1km TIR grid, nadir view\"},\"slstr-cartesian-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        cartesian coordinates\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        land surface temperature (SLSTR LST).\"},{\"id\":\"sentinel-3-slstr-wst-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Sea Surface Temperature Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-wst-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Sea Surface Temperature\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-wst-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Sea Surface Temperature Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-slstr-wst-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2017-10-31T23:59:57.451604Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Temperature\",\"Ocean\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"S7\",\"description\":\"Band
        7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S8\",\"description\":\"Band
        8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band
        9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"slstr-wst\"],\"s3:product_type\":[\"SL_2_WST___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst)
        products containing data on sea surface temperature measurements on a 1km
        grid. Each product consists of a single NetCDF file containing all data variables:\\n\\n-
        Sea Surface Temperature (SST) value\\n- SST total uncertainty\\n- Latitude
        and longitude coordinates\\n- SST time deviation\\n- Single Sensor Error Statistic
        (SSES) bias and standard deviation estimate\\n- Contextual parameters such
        as wind speed at 10 m and fractional sea-ice contamination\\n- Quality flag\\n-
        Satellite zenith angle\\n- Top Of Atmosphere (TOA) Brightness Temperature
        (BT)\\n- TOA noise equivalent BT\\n\\nMore information about the product and
        data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from October 2017 to present.\\n\",\"item_assets\":{\"l2p\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S7\",\"description\":\"Band
        7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S8\",\"description\":\"Band
        8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band
        9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"description\":\"Skin
        Sea Surface Temperature (SST) values\"},\"browse-jpg\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"description\":\"Preview
        image produced by the European Organisation for the Exploitation of Meteorological
        Satellites (EUMETSAT)\"},\"eop-metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"Metadata
        produced by the European Organisation for the Exploitation of Meteorological
        Satellites (EUMETSAT)\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        sea surface temperature (SLSTR WST).\"},{\"id\":\"sentinel-3-sral-wat-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Ocean Radar Altimetry Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-wat-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Ocean Radar Altimetry\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-sral-wat-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Ocean Radar Altimetry Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-sral-wat-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-81.5,180,81.5]]},\"temporal\":{\"interval\":[[\"2017-01-28T00:59:14.149496Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Radar\",\"Altimetry\",\"Ocean\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"SRAL\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"sral-wat\"],\"s3:product_type\":[\"SR_2_WAT___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products)
        products, which contain data on ocean radar altimetry measurements. Each product
        contains three NetCDF files:\\n\\n- A reduced data file containing a subset
        of the 1 Hz Ku-band parameters.\\n- A standard data file containing the standard
        1 Hz and 20 Hz Ku- and C-band parameters.\\n- An enhanced data file containing
        the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms
        and parameters necessary to reprocess the data.\\n\\nMore information about
        the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from January 2017 to present.\\n\",\"item_assets\":{\"eop-metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"Product
        metadata file produced by the European Organisation for the Exploitation of
        Meteorological Satellites (EUMETSAT)\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"reduced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Reduced
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"enhanced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Enhanced
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band
        C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"standard-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Standard
        measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band
        C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band
        Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        radar altimetry over ocean (SRAL WAT).\"},{\"id\":\"hls2-s30\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP
        DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/hls2-s30\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Harmonized
        Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hls2-s30.webp\",\"type\":\"image/webp\",\"title\":\"HLS2
        Sentinel Collection Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/hls2-s30.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W-MON\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2020-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Landsat\",\"HLS\",\"Satellite\",\"Global\",\"Imagery\"],\"providers\":[{\"url\":\"https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2\",\"name\":\"ESA\",\"roles\":[\"producer\"]},{\"url\":\"https://doi.org/10.5067/HLS/HLSL30.002\",\"name\":\"LP
        DAAC\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[30],\"platform\":[\"Sentinel-2A\",\"Sentinel-2B\",\"Sentinel-2C\"]},\"description\":\"Harmonized
        Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance
        (SR)\\nand top of atmosphere (TOA) brightness data from the Operational Land
        Imager (OLI) aboard the\\njoint NASA/USGS Landsat 8 and Landsat 9 satellites
        and the Multi-Spectral Instrument (MSI)\\naboard the ESA (European Space Agency)
        Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\\nThe combined measurement
        enables global observations of the land every 2-3 days at 30 meter\\n(m) spatial
        resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution
        and\\nMilitary Grid Reference System (MGRS) tiling system and are \\\"stackable\\\"
        for time series analysis.\\nThis dataset is in the form of cloud-optimized
        GeoTIFFs. The HLS v2.0 data is generated by NASA's\\nIMPACT team at Marshall
        Space Flight Center. The product latency is 1.7 days, from the satellite\\noverpass
        to the HLS data availability at NASA's Land Processes Distributed Active Archive
        Center\\n(LP DAAC). For more information see LP DAAC's\\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\\n\\nThis
        collection contains HLS data collected from Sentinel-2A, Sentinel-2B, and
        Sentinel-2C.\\nHLS data generated from Landsat can be found in a separate
        collection.\\n\",\"item_assets\":{\"B01\":{\"type\":\"image/tiff; application=geotiff;
        profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Coastal Aerosol\",\"eo:bands\":[{\"name\":\"B01\",\"common_name\":\"coastal\",\"center_wavelength\":0.4439,\"full_width_half_max\":0.027}]},\"B02\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Blue\",\"eo:bands\":[{\"name\":\"B02\",\"common_name\":\"blue\",\"center_wavelength\":0.4966,\"full_width_half_max\":0.098}]},\"B03\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Green\",\"eo:bands\":[{\"name\":\"B03\",\"common_name\":\"green\",\"center_wavelength\":0.56,\"full_width_half_max\":0.045}]},\"B04\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red\",\"eo:bands\":[{\"name\":\"B04\",\"common_name\":\"red\",\"center_wavelength\":0.6645,\"full_width_half_max\":0.038}]},\"B05\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge
        1\",\"eo:bands\":[{\"name\":\"B05\",\"center_wavelength\":0.7039,\"full_width_half_max\":0.019}]},\"B06\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge
        2\",\"eo:bands\":[{\"name\":\"B06\",\"center_wavelength\":0.7402,\"full_width_half_max\":0.018}]},\"B07\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge
        3\",\"eo:bands\":[{\"name\":\"B07\",\"center_wavelength\":0.7825,\"full_width_half_max\":0.028}]},\"B08\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"NIR
        Broad\",\"eo:bands\":[{\"name\":\"B08\",\"common_name\":\"nir\",\"center_wavelength\":0.8351,\"full_width_half_max\":0.145}]},\"B09\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Water
        Vapor\",\"eo:bands\":[{\"name\":\"B09\",\"center_wavelength\":0.945,\"full_width_half_max\":0.026}]},\"B10\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cirrus\",\"eo:bands\":[{\"name\":\"B10\",\"common_name\":\"cirrus\",\"center_wavelength\":1.3735,\"full_width_half_max\":0.075}]},\"B11\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR
        1\",\"eo:bands\":[{\"name\":\"B11\",\"common_name\":\"swir16\",\"center_wavelength\":1.6137,\"full_width_half_max\":0.143}]},\"B12\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR
        2\",\"eo:bands\":[{\"name\":\"B12\",\"common_name\":\"swir22\",\"center_wavelength\":2.22024,\"full_width_half_max\":0.242}]},\"B8A\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"NIR
        Narrow\",\"eo:bands\":[{\"name\":\"B8A\",\"center_wavelength\":0.8648,\"full_width_half_max\":0.033}]},\"SAA\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SAA\",\"eo:bands\":[{\"name\":\"SAA\"}]},\"SZA\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SZA\",\"eo:bands\":[{\"name\":\"SZA\"}]},\"VAA\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VAA\",\"eo:bands\":[{\"name\":\"VAA\"}]},\"VZA\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VZA\",\"eo:bands\":[{\"name\":\"VZA\"}]},\"Fmask\":{\"type\":\"image/tiff;
        application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Fmask\",\"eo:bands\":[{\"name\":\"Fmask\"}]}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"hls2\",\"msft:container\":\"hls2\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\",\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\",\"https://stac-extensions.github.io/view/v1.0.0/schema.json\",\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\"msft:storage_account\":\"hls2euwest\",\"msft:short_description\":\"Harmonized
        Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data\"},{\"id\":\"ms-buildings\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings\"},{\"rel\":\"license\",\"href\":\"https://github.com/microsoft/GlobalMLBuildingFootprints/blob/main/LICENSE\",\"type\":\"text/html\",\"title\":\"ODbL-1.0
        License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/ms-buildings\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Microsoft
        Building Footprints\",\"assets\":{\"delta\":{\"href\":\"az://footprints/delta/2023-04-25/ml-buildings.parquet/\",\"title\":\"Delta
        table\",\"description\":\"Delta table with latest buildings footprints partitioned
        by Region and quadkey.\",\"table:storage_options\":{\"account_name\":\"bingmlbuildings\"}},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/msbuildings-thumbnail.png\",\"type\":\"image/png\",\"title\":\"Thumbnail\"},\"global-footprints\":{\"title\":\"Global
        Bing ML building footprints\",\"href\":\"https://planetarycomputer.microsoft.com/api/data/v1/vector/collections/ms-buildings/tilesets/global-footprints/tilejson.json\",\"type\":\"application/json\",\"roles\":[\"tiles\"]}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,90.0,180.0,-90.0]]},\"temporal\":{\"interval\":[[\"2014-01-01T00:00:00Z\",null]]}},\"license\":\"ODbL-1.0\",\"keywords\":[\"Bing
        Maps\",\"Buildings\",\"geoparquet\",\"Microsoft\",\"Footprint\",\"Delta\"],\"providers\":[{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"producer\",\"processor\",\"host\"]}],\"summaries\":{\"msbuildings:processing-date\":[\"2023-04-25\"]},\"description\":\"Bing
        Maps is releasing open building footprints around the world. We have detected
        over 999 million buildings from Bing Maps imagery between 2014 and 2021 including
        Maxar and Airbus imagery. The data is freely available for download and use
        under ODbL. This dataset complements our other releases.\\n\\nFor more information,
        see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/)
        repository on GitHub.\\n\\n## Building footprint creation\\n\\nThe building
        extraction is done in two stages:\\n\\n1. Semantic Segmentation \u2013 Recognizing
        building pixels on an aerial image using deep neural networks (DNNs)\\n2.
        Polygonization \u2013 Converting building pixel detections into polygons\\n\\n**Stage
        1: Semantic Segmentation**\\n\\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\\n\\n**Stage
        2: Polygonization**\\n\\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\\n\\n##
        Data assets\\n\\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet)
        datasets in [Delta][delta] table format.\\nThe data are partitioned by\\n\\n1.
        Region\\n2. quadkey at [Bing Map Tiles][tiles] level 9\\n\\nEach `(Region,
        quadkey)` pair will have one or more geoparquet files, depending on the density
        of the of the buildings in that area.\\n\\nNote that older items in this dataset
        are *not* spatially partitioned. We recommend using data with a processing
        date\\nof 2023-04-25 or newer. This processing date is part of the URL for
        each parquet file and is captured in the STAC metadata\\nfor each item (see
        below).\\n\\n## Delta Format\\n\\nThe collection-level asset under the `delta`
        key gives you the fsspec-style URL\\nto the Delta table. This can be used
        to efficiently query for matching partitions\\nby `Region` and `quadkey`.
        See the notebook for an example using Python.\\n\\n## STAC metadata\\n\\nThis
        STAC collection has one STAC item per region. The `msbuildings:region`\\nproperty
        can be used to filter items to a specific region, and the `msbuildings:quadkey`\\nproperty
        can be used to filter items to a specific quadkey (though you can also search\\nby
        the `geometry`).\\n\\nNote that older STAC items are not spatially partitioned.
        We recommend filtering on\\nitems with an `msbuildings:processing-date` of
        `2023-04-25` or newer. See the collection\\nsummary for `msbuildings:processing-date`
        for a list of valid values.\\n\\n[delta]: https://delta.io/\\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Building
        Footprints\",\"description\":\"Parquet dataset with the building footprints
        for this region.\",\"table:storage_options\":{\"account_name\":\"bingmlbuildings\"}}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"geometry\",\"type\":\"byte_array\",\"description\":\"Building
        footprint polygons\"}],\"msft:container\":\"footprints\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"],\"msft:storage_account\":\"bingmlbuildings\",\"msft:short_description\":\"Machine
        learning detected buildings footprints.\"},{\"id\":\"sentinel-3-slstr-frp-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Fire Radiative Power Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-frp-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Fire Radiative Power\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-frp-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Fire Radiative Power Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-slstr-frp-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2020-08-08T23:11:15.617203Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Temperature\",\"Fire\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"F1\",\"description\":\"Band
        10 - Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S5\",\"description\":\"Band
        5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"S7\",\"description\":\"Band
        7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"slstr-frp\"],\"s3:product_type\":[\"SL_2_FRP___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp)
        (FRP) products containing data on fires detected over land and ocean.\\n\\n##
        Data files\\n\\nThe primary measurement data is contained in the `FRP_in.nc`
        file and provides FRP and uncertainties, projected onto a 1km grid, for fires
        detected in the thermal infrared (TIR) spectrum over land. Since February
        2022, FRP and uncertainties are also provided for fires detected in the short
        wave infrared (SWIR) spectrum over both land and ocean, with the delivered
        data projected onto a 500m grid. The latter SWIR-detected fire data is only
        available for night-time measurements and is contained in the `FRP_an.nc`
        or `FRP_bn.nc` files.\\n\\nIn addition to the measurement data files, a standard
        set of annotation data files provide meteorological information, geolocation
        and time coordinates, geometry information, and quality flags.\\n\\n## Processing\\n\\nThe
        TIR fire detection is based on measurements from the S7 and F1 bands of the
        [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument);
        SWIR fire detection is based on the S5 and S6 bands. More information about
        the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from August 2020 to present.\\n\",\"item_assets\":{\"frp-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S5\",\"description\":\"Band
        5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"S7\",\"description\":\"Band
        7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"F1\",\"description\":\"Band
        10 - Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398}],\"description\":\"Fire
        Radiative Power (FRP) dataset\"},\"slstr-met-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Meteorological
        parameters regridded onto the 16km tie points\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"slstr-time-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time
        annotations for the 1 KM grid\"},\"slstr-flags-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Global
        flags for the 1km F1 grid, nadir view\"},\"slstr-flags-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Global
        flags for the 1km TIR grid, nadir view\"},\"slstr-indices-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Scan,
        pixel and detector annotations for the 1km F1 grid, nadir view\"},\"slstr-indices-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Scan,
        pixel and detector annotations for the 1km TIR grid, nadir view\"},\"slstr-geodetic-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution geodetic coordinates for the 1km F1 grid, nadir view\"},\"slstr-geodetic-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution geodetic coordinates for the 1km TIR grid, nadir view\"},\"slstr-geodetic-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        geodetic coordinates\"},\"slstr-geometry-tn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        solar and satellite geometry annotations, nadir view\"},\"slstr-cartesian-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution cartesian coordinates for the 1km F1 grid, nadir view\"},\"slstr-cartesian-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full
        resolution cartesian coordinates for the 1km TIR grid, nadir view\"},\"slstr-cartesian-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km
        cartesian coordinates\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        fire detection over land (SLSTR FRP).\"},{\"id\":\"sentinel-3-synergy-syn-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Land Surface Reflectance and Aerosol Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-syn-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Land Surface Reflectance and Aerosol\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-syn-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Land Surface Reflectance and Aerosol Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-syn-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2018-09-22T16:51:00.001276Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Land\",\"Reflectance\",\"Aerosol\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa13\",\"description\":\"Band
        13 - O2 absorption / aerosol correction\",\"center_wavelength\":0.76125,\"full_width_half_max\":0.0025},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04},{\"name\":\"S1\",\"description\":\"Band
        1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band
        2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band
        3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S4\",\"description\":\"Band
        4 - Cirrus detection over land\",\"center_wavelength\":1.3748,\"full_width_half_max\":0.0208},{\"name\":\"S5\",\"description\":\"Band
        5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"SYN01\",\"description\":\"OLCI
        channel Oa01\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"SYN02\",\"description\":\"OLCI
        channel Oa02\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"SYN03\",\"description\":\"OLCI
        channel Oa03\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"SYN04\",\"description\":\"OLCI
        channel Oa04\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"SYN05\",\"description\":\"OLCI
        channel Oa05\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"SYN06\",\"description\":\"OLCI
        channel Oa06\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"SYN07\",\"description\":\"OLCI
        channel Oa07\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"SYN08\",\"description\":\"OLCI
        channel Oa08\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"SYN09\",\"description\":\"OLCI
        channel Oa09\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"SYN10\",\"description\":\"OLCI
        channel Oa10\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"SYN11\",\"description\":\"OLCI
        channel Oa11\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"SYN12\",\"description\":\"OLCI
        channel Oa12\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"SYN13\",\"description\":\"OLCI
        channel Oa16\",\"center_wavelength\":0.7785,\"full_width_half_max\":0.015},{\"name\":\"SYN14\",\"description\":\"OLCI
        channel Oa17\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN15\",\"description\":\"OLCI
        channel Oa18\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"SYN16\",\"description\":\"OLCI
        channel Oa21\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04},{\"name\":\"SYN17\",\"description\":\"SLSTR
        nadir channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02},{\"name\":\"SYN18\",\"description\":\"SLSTR
        nadir channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN19\",\"description\":\"SLSTR
        nadir channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN20\",\"description\":\"SLSTR
        nadir channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN21\",\"description\":\"SLSTR
        nadir channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05},{\"name\":\"SYN22\",\"description\":\"SLSTR
        oblique channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02},{\"name\":\"SYN23\",\"description\":\"SLSTR
        oblique channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN24\",\"description\":\"SLSTR
        oblique channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN25\",\"description\":\"SLSTR
        oblique channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN26\",\"description\":\"SLSTR
        oblique channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\",\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"synergy-syn\"],\"s3:product_type\":[\"SY_2_SYN___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance
        and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn)
        product, which contains data on Surface Directional Reflectance, Aerosol Optical
        Thickness, and an Angstrom coefficient estimate over land.\\n\\n## Data Files\\n\\nIndividual
        NetCDF files for the following variables:\\n\\n- Surface Directional Reflectance
        (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr)
        channels (S1 to S6 for both nadir and oblique views, except S4) and for all
        [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci)
        channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the
        water vapor bands Oa19 and Oa20.\\n- Aerosol optical thickness at 550nm with
        error estimates.\\n- Angstrom coefficient at 550nm.\\n\\nMore information
        about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from September 2018 to present.\\n\",\"item_assets\":{\"time\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time
        stamps annotation\"},\"syn-amin\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"L2
        Aerosol model index number data\"},\"syn-flags\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Classification
        and quality Flags associated with OLCI, SLSTR and SYNERGY products\"},\"syn-ato550\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S1\",\"description\":\"Band
        1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band
        2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band
        3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S5\",\"description\":\"Band
        5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Aerosol
        Optical Thickness Data Set\"},\"geolocation\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"High
        resolution georeferencing data\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"},\"tiepoints-olci\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Low
        resolution georeferencing data and Sun and View angles associated with OLCI
        products\"},\"tiepoints-meteo\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"ECMWF
        meteorology data\"},\"tiepoints-slstr-n\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Low
        resolution georeferencing data and Sun and View angles associated with SLSTR
        nadir view products\"},\"tiepoints-slstr-o\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Low
        resolution georeferencing data and Sun and View angles associated with SLSTR
        oblique view products\"},\"syn-angstrom-exp550\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S1\",\"description\":\"Band
        1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band
        2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band
        3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S5\",\"description\":\"Band
        5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Aerosol
        Angstrom Exponent Data Set\"},\"syn-s1n-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN17\",\"description\":\"SLSTR
        nadir channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 01 acquired in nadir
        view\"},\"syn-s1o-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN22\",\"description\":\"SLSTR
        oblique channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 01 acquired in oblique
        view\"},\"syn-s2n-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN18\",\"description\":\"SLSTR
        nadir channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 02 acquired in nadir
        view\"},\"syn-s2o-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN23\",\"description\":\"SLSTR
        oblique channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 02 acquired in oblique
        view\"},\"syn-s3n-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN19\",\"description\":\"SLSTR
        nadir channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 03 acquired in nadir
        view\"},\"syn-s3o-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN24\",\"description\":\"SLSTR
        oblique channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 03 acquired in oblique
        view\"},\"syn-s5n-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN20\",\"description\":\"SLSTR
        nadir channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 05 acquired in nadir
        view\"},\"syn-s5o-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN25\",\"description\":\"SLSTR
        oblique channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 05 acquired in oblique
        view\"},\"syn-s6n-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN21\",\"description\":\"SLSTR
        nadir channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 06 acquired in nadir
        view\"},\"syn-s6o-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN26\",\"description\":\"SLSTR
        oblique channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05}],\"description\":\"Surface
        directional reflectance associated with SLSTR channel 06 acquired in oblique
        view\"},\"syn-oa01-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN01\",\"description\":\"OLCI
        channel Oa01\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 01\"},\"syn-oa02-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN02\",\"description\":\"OLCI
        channel Oa02\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 02\"},\"syn-oa03-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN03\",\"description\":\"OLCI
        channel Oa03\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 03\"},\"syn-oa04-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN04\",\"description\":\"OLCI
        channel Oa04\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 04\"},\"syn-oa05-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN05\",\"description\":\"OLCI
        channel Oa05\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 05\"},\"syn-oa06-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN06\",\"description\":\"OLCI
        channel Oa06\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 06\"},\"syn-oa07-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN07\",\"description\":\"OLCI
        channel Oa07\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 07\"},\"syn-oa08-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN08\",\"description\":\"OLCI
        channel Oa08\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 08\"},\"syn-oa09-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN09\",\"description\":\"OLCI
        channel Oa09\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 09\"},\"syn-oa10-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN10\",\"description\":\"OLCI
        channel Oa10\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 10\"},\"syn-oa11-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN11\",\"description\":\"OLCI
        channel Oa11\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 11\"},\"syn-oa12-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN12\",\"description\":\"OLCI
        channel Oa12\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 12\"},\"syn-oa16-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN13\",\"description\":\"OLCI
        channel Oa16\",\"center_wavelength\":0.7785,\"full_width_half_max\":0.015}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 16\"},\"syn-oa17-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN14\",\"description\":\"OLCI
        channel Oa17\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 17\"},\"syn-oa18-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN15\",\"description\":\"OLCI
        channel Oa18\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 18\"},\"syn-oa21-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN16\",\"description\":\"OLCI
        channel Oa21\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Surface
        directional reflectance associated with OLCI channel 21\"},\"syn-sdr-removed-pixels\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S1\",\"description\":\"Band
        1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band
        2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band
        3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S4\",\"description\":\"Band
        4 - Cirrus detection over land\",\"center_wavelength\":1.3748,\"full_width_half_max\":0.0208},{\"name\":\"S6\",\"description\":\"Band
        6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"Oa01\",\"description\":\"Band
        1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band
        2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band
        3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band
        4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band
        5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band
        6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band
        7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band
        8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band
        9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band
        10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band
        11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band
        12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa13\",\"description\":\"Band
        13 - O2 absorption / aerosol correction\",\"center_wavelength\":0.76125,\"full_width_half_max\":0.0025},{\"name\":\"Oa16\",\"description\":\"Band
        16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band
        17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band
        18 - Water vapour absorption reference. Common reference band with SLSTR.
        Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band
        19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band
        21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Surface
        directional reflectance and aerosol parameters associated with removed pixel\"},\"annotations-removed-pixels\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Annotations
        parameters associated with removed pixel\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        surface reflectance and aerosol over land (SYNERGY SYN).\"},{\"id\":\"sentinel-3-synergy-vgp-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vgp-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vgp-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        Top of Atmosphere Reflectance Product (SPOT VEGETATION) User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-vgp-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        Top of Atmosphere Reflectance (SPOT VEGETATION)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-vgp-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        Top of Atmosphere Reflectance (SPOT VEGETATION) Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-vgp-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2018-10-08T08:09:40.491227Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Reflectance\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
        channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR
        nadir and oblique channels S5, S6\",\"center_wavelength\":1.665,\"full_width_half_max\":0.085}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\",\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"synergy-vgp\"],\"s3:product_type\":[\"SY_2_VGP___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp)
        product, which is a SPOT VEGETATION Continuity Product containing measurement
        data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments)
        onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top
        of atmosphere reflectance bands:\\n\\n- B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n-
        B3 (NIR, 835nm)\\n- MIR (SWIR, 1665nm)\\n\\nThe four reflectance bands have
        center wavelengths matching those on the original SPOT VEGETATION instrument
        and have been adapted for scientific applications requiring highly accurate
        physical measurements through correction for systematic errors and re-sampling
        to predefined geographic projections. The pixel brightness count is the ground
        area's apparent reflectance as seen at the top of atmosphere.\\n\\n## Data
        files\\n\\nNetCDF files are provided for the four reflectance bands. Additional
        metadata are delivered in annotation NetCDF files, each containing a single
        variable, including the geometric viewing and illumination conditions, the
        total water vapour and ozone columns, and the aerosol optical depth.\\n\\nMore
        information about the product and data processing can be found in the [User
        Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from October 2018 to present.\\n\",\"item_assets\":{\"ag\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Aerosol
        optical thickness data\"},\"b0\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
        channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02}],\"description\":\"Top
        of atmosphere reflectance data set associated with the VGT-B0 channel\"},\"b2\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035}],\"description\":\"Top
        of atmosphere reflectance data set associated with the VGT-B2 channel\"},\"b3\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055}],\"description\":\"Top
        of atmosphere reflectance data set associated with the VGT-B3 channel\"},\"og\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Total
        ozone column data\"},\"sm\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Status
        map data\"},\"mir\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"MIR\",\"description\":\"SLSTR
        nadir and oblique channels S5, S6\",\"center_wavelength\":1.665,\"full_width_half_max\":0.085}],\"description\":\"Top
        of atmosphere Reflectance data set associated with the VGT-MIR channel\"},\"saa\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Solar
        azimuth angle data\"},\"sza\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Solar
        zenith angle data\"},\"vaa\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"View
        azimuth angle data\"},\"vza\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"View
        zenith angle data\"},\"wvg\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Total
        column water vapour data\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE
        product manifest\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"sentinel-3\",\"msft:container\":\"sentinel-3\",\"stac_extensions\":[\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\",\"https://stac-extensions.github.io/table/v1.2.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"],\"msft:storage_account\":\"sentinel3euwest\",\"msft:short_description\":\"Sentinel-3
        top of atmosphere reflectance (SYNERGY VGP, a SPOT VEGETATION Continuity Product).\"},{\"id\":\"sentinel-3-synergy-vg1-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vg1-l2-netcdf/items\"},{\"rel\":\"parent\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/\"},{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vg1-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel
        Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10\",\"type\":\"text/html\",\"title\":\"Sentinel-3
        1-Day Surface Reflectance and NDVI Product (SPOT VEGETATION) User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-vg1-l2-netcdf\",\"title\":\"Human
        readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3
        1-Day Surface Reflectance and NDVI (SPOT VEGETATION)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-vg1-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3
        1-Day Surface Reflectance and NDVI (SPOT VEGETATION) Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-vg1-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet
        STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported
        to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-56.0,180.0,75.0]]},\"temporal\":{\"interval\":[[\"2018-10-04T23:17:21Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Reflectance\",\"NDVI\"],\"providers\":[{\"url\":\"https://earth.esa.int/web/guest/home\",\"name\":\"ESA\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
        channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI
        channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI
        channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR
        nadir and oblique channels S5, S6\",\"center_wavelength\":1.665,\"full_width_half_max\":0.085}],\"platform\":[\"Sentinel-3A\",\"Sentinel-3B\"],\"instruments\":[\"OLCI\",\"SLSTR\"],\"constellation\":[\"Sentinel-3\"],\"s3:product_name\":[\"synergy-vg1\"],\"s3:product_type\":[\"SY_2_VG1___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This
        Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance
        and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)
        products, which are SPOT VEGETATION Continuity Products similar to those obtained
        from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments)
        onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum
        Normalized Difference Vegetation Index (NDVI) composite, which is derived
        from daily ground reflecrtance, and four surface reflectance bands:\\n\\n-
        B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n- B3 (NIR, 835nm)\\n- MIR (SWIR, 1665nm)\\n\\nThe
        four reflectance bands have center wavelengths matching those on the original
        SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the
        amount of vegetation, is derived from the B3 and B2 bands.\\n\\n## Data files\\n\\nThe
        four reflectance bands and NDVI values are each contained in dedicated NetCDF
        files. Additional metadata are delivered in annotation NetCDF files, each
        containing a single variable, including the geometric viewing and illumination
        conditions, the total water vapour and ozone columns, and the aerosol optical
        depth.\\n\\nEach 1-day product is delivered as a set of 10 rectangular scenes:\\n\\n-
        AFRICA\\n- NORTH_AMERICA\\n- SOUTH_AMERICA\\n- CENTRAL_AMERICA\\n- NORTH_ASIA\\n-
        WEST_ASIA\\n- SOUTH_EAST_ASIA\\n- ASIAN_ISLANDS\\n- AUSTRALASIA\\n- EUROPE\\n\\nMore
        information about the product and data processing can be found in the [User
        Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)
        and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\\n\\nThis
        Collection contains Level-2 data in NetCDF files from October 2018 to present.\\n\",\"item_assets\":{\"ag\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Aerosol
        optical thickness data\"},\"b0\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI
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