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  • A Fusion Dataset for Crop Type Classification in Germany

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.xml
    Description:

    This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 52.4179888 13.6339485 52.8494418 14.3529903

    MLHUB Short Name: A Fusion Dataset for Crop Type Classification in Germany Version ID: 1 Unique ID: C2781412484-MLHUB

    This collection does not contain any granules

  • A Fusion Dataset for Crop Type Classification in Western Cape, South Africa

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412697-MLHUB.xml
    Description:

    This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -34.413256 20.5212157 -33.9796334 21.043415

    MLHUB Short Name: A Fusion Dataset for Crop Type Classification in Western Cape, South Africa Version ID: 1 Unique ID: C2781412697-MLHUB

    This collection does not contain any granules

  • ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019

    https://cmr.earthdata.nasa.gov/search/concepts/C2482179223-ORNL_CLOUD.xml
    Description:

    This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 58.25 -146.43 66.81 -110.92

    ORNL_CLOUD Short Name: InundationMap_YkFlats_PeaceAth_1901 Version ID: 1 Unique ID: C2482179223-ORNL_CLOUD

  • ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2240727799-ORNL_CLOUD.xml
    Description:

    This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 57.77 -115.29 61.79 -109.64

    ORNL_CLOUD Short Name: Ecosystem_Map_SRD_PAD_1947 Version ID: 1 Unique ID: C2240727799-ORNL_CLOUD

  • Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020

    https://cmr.earthdata.nasa.gov/search/concepts/C3126460246-ORNL_CLOUD.xml
    Description:

    This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 24.52 -124.74 49 -66.93

    ORNL_CLOUD Short Name: Salt_Marsh_Biomass_CONUS_2348 Version ID: 1 Unique ID: C3126460246-ORNL_CLOUD

  • Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015

    https://cmr.earthdata.nasa.gov/search/concepts/C2345876612-ORNL_CLOUD.xml
    Description:

    This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 25.09 -122.73 47.12 -69.93

    ORNL_CLOUD Short Name: Tidal_Marsh_Biomass_US_V1-1_1879 Version ID: 1.1 Unique ID: C2345876612-ORNL_CLOUD

  • Annual Land Use and Urban Land Cover: Ethiopia, Nigeria, and South Africa, 2016-2020

    https://cmr.earthdata.nasa.gov/search/concepts/C3235688636-ORNL_CLOUD.xml
    Description:

    This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -35.34 2.57 16.21 49.69

    ORNL_CLOUD Short Name: LULC_Nigeria_Ethiopia_SAfrica_2367 Version ID: 1 Unique ID: C3235688636-ORNL_CLOUD

  • ARIA Sentinel-1 Geocoded Unwrapped Interferograms

    https://cmr.earthdata.nasa.gov/search/concepts/C2859376221-ASF.xml
    Description:

    Level-2 interferometric products generated by the Jet Propulsion Lab (JPL) ARIA project. The creation, discovery, and distribution of these products support InSAR science around tectonically active regions, volcanoes, or areas of subsidence/uplift. The generation of the ARIA-S1-GUNW products was in part funded through collaborations with the AWS Open Data Program and NASA ROSES.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -90 -180 90 180

    ASF Short Name: ARIA_S1_GUNW Version ID: 1 Unique ID: C2859376221-ASF

  • Automated Greenland Glacier Termini Position Time Series, Version 1

    https://cmr.earthdata.nasa.gov/search/concepts/C2849438379-NSIDCV0.xml
    Description:

    This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the <a href="https://nsidc.org/data/nsidc-0714/versions/1">Greenland Ice Sheet Mapping Project (GrIMP) ice masks</a>.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 57 -75 85 -8

    NSIDCV0 Short Name: NSIDC-0788 Version ID: 1 Unique ID: C2849438379-NSIDCV0

    This collection does not contain any granules

  • Cloud to Street - Microsoft flood dataset

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412798-MLHUB.xml
    Description:

    The C2S-MS Floods Dataset is a dataset of global flood events with labeled Sentinel-1 & Sentinel-2 pairs. There are 900 sets (1800 total) of near-coincident Sentinel-1 and Sentinel-2 chips (512 x 512 pixels) from 18 global flood events. Each chip contains a water label for both Sentinel-1 and Sentinel-2, as well as a cloud/cloud shadow mask for Sentinel-2. The dataset was constructed by Cloud to Street in collaboration with and funded by the Microsoft Planetary Computer team.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -25.250962 -96.631888 48.745167 141.118143

    MLHUB Short Name: Cloud to Street - Microsoft flood dataset Version ID: 1 Unique ID: C2781412798-MLHUB

    This collection does not contain any granules