OpenSearch

Using the NASA EOSDIS Common Metadata Repository

Collection Search

  • 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

  • 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

  • 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 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

  • 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.

    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

  • 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

  • CMS: Mangrove Forest Cover Extent and Change across Major River Deltas, 2000-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2389022166-ORNL_CLOUD.xml
    Description:

    This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -28 -82 22.5 107.03

    ORNL_CLOUD Short Name: CMS_Mangrove_Cover_1670 Version ID: 1.1 Unique ID: C2389022166-ORNL_CLOUD

  • Daily Great Lakes Ice Concentration, 1973 Onward, Version 1

    https://cmr.earthdata.nasa.gov/search/concepts/C1712034199-NSIDCV0.xml
    Description:

    This data set consists of daily gridded lake ice concentration for the Laurentian Great Lakes from the NOAA Great Lakes Environmental Research Laboratory (GLERL). The data are provided as gridded ASCII text files and shapefiles along with corresponding browse image files in .jpg format.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 38.86 -92.42 50.6 -75.86

    NSIDCV0 Short Name: G10029 Version ID: 1 Unique ID: C1712034199-NSIDCV0