ed735798-c133-44f1-a977-ad0c0dcad514 urn:uuid Cropland Data Layer - All years and states USDA, NASS, Spatial Analysis Research Section 800-727-9540 855-493-0447 1400 Independence Avenue, SW, Room 5029 South Building Washington District of Columbia 20250-2001 USA HQ_RDD_GIB@usda.gov 2025-03-04T22:40:02.192Z 2023-08-18T21:38:08.043Z ISO 19115-3 Geographic information -- Metadata -- Part 3: XML schema implementation for fundamental concepts ISO 19115-3:2018(E) https://geodata.nal.usda.gov:/geonetwork/srv/api/records/45c391c4-8a7c-4617-9bee-4ada7baaec6e 2 289567 461431 EPSG:5070 NAD83 / Conus Albers (EPSG:5070) Cropland Data Layer NGDA109 2025-02-27 Annual ongoing series United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) USDA NASS Marketing and Information Services Office, Washington, D.C. https://www.nass.usda.gov/ WWW:LINK-1.0-http--link Main web page for USDA National Agricultural Statistics Service United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) https://croplandcros.scinet.usda.gov/ WWW:LINK-1.0-http--link CroplandCROS https://datagateway.nrcs.usda.gov WWW:LINK-1.0-http--link USDA National Resources Conservation Service, Geospatial Data Gateway https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php WWW:LINK-1.0-http--link NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website Cropland Data Frequently asked questions (FAQ's) https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/CDL_browse_graphic_752x600.png PNG https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/CDL_browse_graphic_752x600.png WWW:DOWNLOAD-1.0-http--download The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer. Traditionally the CDL has been a 30-meter product for the years 1997-2023. Beginning in 2024, the CDL is a 10-meter product. The 2024 CDL crop classification utilized remote sensing data from harmonized Sentinel-2 MSI Level-2A, Landsat 8, and Landsat 9 Level-2 Collection 2 Tier-1 products, providing surface reflectance (SR) data across multiple spectral bands, including GREEN, RED, NIR, SWIR1, SWIR2, and RedEdge bands 1-4. To mitigate cloud cover, 10-day median composites of surface reflectance and NDVI were created from the cloud-masked Landsat-Sentinel multi-sensor data for the growing season of 2024. An impervious layer from USGS NLCD 2021 and a digital elevation model from USGS 3DEP were also included ancillary input variables. In addition, mixed sampling strategies and localized training and were applied to the 2024 10m CDL production. Additional information: Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079. PDF available at <https://www.nass.usda.gov/Research_and_Science/Cropland/docs/IGARSS2024_Proceedings_10mCDL_Li_etal.pdf>. The 2024 CDL has a spatial resolution of 10 meters and was produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected throughout the growing season. Additional ancillary inputs were used to supplement and improve the land cover classification including the United States Geological Survey (USGS) 3D Elevation Program (3DEP) Elevation Dataset (NED), and the USGS National Land Cover Database imperviousness data layer. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. Some CDL states incorporate additional crop-specific ground reference obtained from the following non-FSA sources which are detailed in the 'Lineage' Section of this metadata: US Bureau of Reclamation, NASS Citrus Data Layer (internal use only), California Department of Water Resources, Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy, Cornell University grape/vineyard data, Utah Department of Water Resources, and Washington State Department of Agriculture. The 2019 NLCD was used as non-agricultural training and validation data for the 2024 CDL. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view a complete list of imagery, ancillary inputs, and ground reference used for a specific state and year. The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide planted acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products. USDA, National Agricultural Statistics Service USDA, NASS, Spatial Analysis Research Section 800-727-9540 855-493-0447 1400 Independence Avenue, SW, Room 5029 South Building Washington District of Columbia 20250-2001 USA HQ_RDD_GIB@usda.gov farming environment imageryBaseMapsEarthCover -130.2328 -63.6722 21.7423 52.8510 Continental US coverage (2008 to current), partial US coverage (1997-2007) 1997-01-01 2025-02-27 Cropland Data Layer - all years and states https://geodata.nal.usda.gov:/geonetwork/srv/api/records/ed735798-c133-44f1-a977-ad0c0dcad514/attachments/CDL_browse_graphic_752x600%5B1%5D.png NGDA National Geospatial Data Asset NGDA109 Land Use Land Cover Theme NGDA Portfolio Themes Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands Earth Science > Land Surface > Land Use/Land Cover > Land Cover Global Change Master Directory (GCMD) Science Keywords crop cover cropland agriculture farming land cover DEIMOS-1 ISRO ResourceSat-2 LISS-3 ESA SENTINEL-2 Landsat CroplandCROS Other Keywords Terms not selected from a predefined list Continent > North America > United States of America Global Change Master Directory (GCMD) Location Keywords Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. NASS maintains a Frequently Asked Questions (FAQ's) webpage at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The USDA NASS Cropland Data Layer and the data offered on the CroplandCROS website is provided to the public as is and is considered public domain and free to redistribute. The USDA NASS does not warrant any conclusions drawn from these data. Access Constraints: None None None The 2024 CDL used the following: Microsoft Windows 10 Enterprise; Google Earth Engine <https://earthengine.google.com/>; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>. The 2024 CDL is the first time using Google Earth Engine to produce the land cover classification. ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. The CDL methodology from 2007 to 2023 used Rulequest See5.0 software to create a decision-tree based classifier. The NLCD Mapping Tool was used to apply the See5.0 decision-tree via ERDAS Imagine. Pre-2007 CDLs were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view a complete list of imagery, ancillary inputs, and ground reference used for a specific state and year. Entity and Attribute Overview: The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. Entity and Attribute Detail Citation: The categories and codes listed here are standardized through all historical CDLs and all states. Not all codes will appear in each state. Inputs have also changed over time, so the absence of a crop type in older CDLs does not necessarily mean it is not present, but rather the category was added in later years. If the following table does not display properly, then please visit the following website to view the information at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. > Data Dictionary: USDA, National Agricultural Statistics Service, Cropland Data Layer > > Source: USDA, National Agricultural Statistics Service > > The following is a cross reference list of the categorization codes and land covers. > Note that not all land cover categories listed below will appear in an individual state. > > Raster > Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0 > > Categorization Code Land Cover > "0" Background > > Raster > Attribute Domain Values and Definitions: CROPS 1-60 > > Categorization Code Land Cover > "1" Corn > "2" Cotton > "3" Rice > "4" Sorghum > "5" Soybeans > "6" Sunflower > "10" Peanuts > "11" Tobacco > "12" Sweet Corn > "13" Pop or Orn Corn > "14" Mint > "21" Barley > "22" Durum Wheat > "23" Spring Wheat > "24" Winter Wheat > "25" Other Small Grains > "26" Dbl Crop WinWht/Soybeans > "27" Rye > "28" Oats > "29" Millet > "30" Speltz > "31" Canola > "32" Flaxseed > "33" Safflower > "34" Rape Seed > "35" Mustard > "36" Alfalfa > "37" Other Hay/Non Alfalfa > "38" Camelina > "39" Buckwheat > "41" Sugarbeets > "42" Dry Beans > "43" Potatoes > "44" Other Crops > "45" Sugarcane > "46" Sweet Potatoes > "47" Misc Vegs & Fruits > "48" Watermelons > "49" Onions > "50" Cucumbers > "51" Chick Peas > "52" Lentils > "53" Peas > "54" Tomatoes > "55" Caneberries > "56" Hops > "57" Herbs > "58" Clover/Wildflowers > "59" Sod/Grass Seed > "60" Switchgrass > > Raster > Attribute Domain Values and Definitions: NON-CROP 61-65 > > Categorization Code Land Cover > "61" Fallow/Idle Cropland > "63" Forest > "64" Shrubland > "65" Barren > > Raster > Attribute Domain Values and Definitions: CROPS 66-80 > > Categorization Code Land Cover > "66" Cherries > "67" Peaches > "68" Apples > "69" Grapes > "70" Christmas Trees > "71" Other Tree Crops > "72" Citrus > "74" Pecans > "75" Almonds > "76" Walnuts > "77" Pears > > Raster > Attribute Domain Values and Definitions: OTHER 81-109 > > Categorization Code Land Cover > "81" Clouds/No Data > "82" Developed > "83" Water > "87" Wetlands > "88" Nonag/Undefined > "92" Aquaculture > > Raster > Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195 > > Categorization Code Land Cover > "111" Open Water > "112" Perennial Ice/Snow > "121" Developed/Open Space > "122" Developed/Low Intensity > "123" Developed/Med Intensity > "124" Developed/High Intensity > "131" Barren > "141" Deciduous Forest > "142" Evergreen Forest > "143" Mixed Forest > "152" Shrubland > "176" Grassland/Pasture > "190" Woody Wetlands > "195" Herbaceous Wetlands > > Raster > Attribute Domain Values and Definitions: CROPS 195-255 > > Categorization Code Land Cover > "204" Pistachios > "205" Triticale > "206" Carrots > "207" Asparagus > "208" Garlic > "209" Cantaloupes > "210" Prunes > "211" Olives > "212" Oranges > "213" Honeydew Melons > "214" Broccoli > "215" Avocados > "216" Peppers > "217" Pomegranates > "218" Nectarines > "219" Greens > "220" Plums > "221" Strawberries > "222" Squash > "223" Apricots > "224" Vetch > "225" Dbl Crop WinWht/Corn > "226" Dbl Crop Oats/Corn > "227" Lettuce > "228" Dbl Crop Triticale/Corn > "229" Pumpkins > "230" Dbl Crop Lettuce/Durum Wht > "231" Dbl Crop Lettuce/Cantaloupe > "232" Dbl Crop Lettuce/Cotton > "233" Dbl Crop Lettuce/Barley > "234" Dbl Crop Durum Wht/Sorghum > "235" Dbl Crop Barley/Sorghum > "236" Dbl Crop WinWht/Sorghum > "237" Dbl Crop Barley/Corn > "238" Dbl Crop WinWht/Cotton > "239" Dbl Crop Soybeans/Cotton > "240" Dbl Crop Soybeans/Oats > "241" Dbl Crop Corn/Soybeans > "242" Blueberries > "243" Cabbage > "244" Cauliflower > "245" Celery > "246" Radishes > "247" Turnips > "248" Eggplants > "249" Gourds > "250" Cranberries > "254" Dbl Crop Barley/Soybeans 0 GeoTIFF 2024 USDA, NASS Customer Service 800-727-9540 855-493-0447 1400 Independence Avenue, SW, Room 5038-S Washington District of Columbia 20250-9410 USA HQ_RDD_GIB@usda.gov Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540. The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>, the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>, and the NASS CDL website <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/>. The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>, the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>, and the NASS CDL website <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/>. Distribution questions can be directed to the NASS Customer Service Hotline at 1-800-727-9540. https://croplandcros.scinet.usda.gov/ https://geodata.nal.usda.gov:/geonetwork/srv/api/records/ed735798-c133-44f1-a977-ad0c0dcad514/attachments/cdl_data-invDatGov2023-10-26ca-pw.json WWW:LINK cdl_data-invDatGov2023-10-26ca-pw.json data.json for metadata Horizontal Positional Accuracy Report The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 and 9 OLI/TIRS imagery is obtained via download from the USGS Global Visualization Viewer <https://glovis.usgs.gov/> using the Collection 2 Level-1 specifications. Please reference the metadata on the Glovis website for the positional accuracy of each Landsat scene. The Sentinel 2A and 2B imagery is obtained via download from the Copernicus Open Access Hub <https://www.copernicus.eu/en> using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please reference the metadata on the Copernicus website for positional accuracy details. The official NASS metadata webpage has additional details. The entire Continental United States is covered by the 2008 to 2024 Cropland Data Layer (CDL). Older CDLs from 1997-2007 are partial coverage. Check the official NASS metadata webpage for additional details. <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted on the NASS metadata webpage <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. Quantitative Attribute Accuracy Assessment The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix. Below are the Overall Accuracy metrics for the crop-specific categories for the Continental United States 2016 to 2024 CDLs. Full accuracy reports for past years, states, and individual crop types are available at the official NASS metadata website at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. 2024 Cropland Data Layer 77.5% OVERALL CROP ACCURACY, 22.5% ERROR, 0.733 KAPPA 2023 Cropland Data Layer 81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.788 KAPPA 2022 Cropland Data Layer 80.9% OVERALL CROP ACCURACY, 19.1% ERROR, 0.780 KAPPA 2021 Cropland Data Layer 81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.787 KAPPA 2020 Cropland Data Layer 81.3% OVERALL CROP ACCURACY, 18.7% ERROR, 0.786 KAPPA 2019 Cropland Data Layer 81.5% OVERALL CROP ACCURACY, 18.5% ERROR, 0.789 KAPPA 2018 Cropland Data Layer 82.3% OVERALL CROP ACCURACY, 17.7% ERROR, 0.796 KAPPA 2017 Cropland Data Layer 82.2% OVERALL CROP ACCURACY, 17.8% ERROR, 0.800 KAPPA 2016 Cropland Data Layer 79.6% OVERALL CROP ACCURACY, 20.4% ERROR, 0.767 KAPPA Note: The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is dependent upon the most current publicly available USGS, National Land Cover Database (NLCD). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>. Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. Full accuracy reports for each year, state, and crop type are available at the official NASS metadata website at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. meters Source Contribution: Raw data used in land cover spectral signature analysis Source Type: online download ResourceSat-2 LISS-3 2017 Indian Space Research Organization (ISRO) Indian Space Research Organisation HQ Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231 Indian Space Research Organization (ISRO) The CDL used ISRO ResourceSat-2 LISS-3 as one of the satellite imagery inputs from the years 2017-2022. The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. For the CDL Program, the imagery was resampled to 30 meters to match the USGS Landsat spatial resolution. The resample used cubic convolution, rigorous transformation. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific scene date, path, row and quadrants used as classification inputs for each state and year. 2017-01-01 2021-12-31 Source Contribution: Raw data used in land cover spectral signature analysisSource Type: online download SENTINEL-2 2024 Copernicus - European Commission European Commission Brussels (Belgium) European Space Agency (ESA) The CDL used Sentinel-2 satellite imagery as one of the inputs from 2017-2024. The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. For the CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific scene date, path, row and quadrants used as classification inputs for each state and year. 2017-01-01 2024-12-31 Source Contribution: Raw data used in land cover spectral signature analysisSource Type: online download DEIMOS-1 2011 Astrium GEO Information Services Elecnor Deimos Imaging Valladolid, Spain Elecnor Deimos Imaging The CDL used DEIMOS-1 and UK-2 satellite imagery as one of the inputs from 2011 to 2020. The DEIMOS-1 and UK-2 satellite sensors operate in three spectral bands at a spatial resolution of 22 meters. Additional information can be obtained at <https://www.deimos-imaging.com/>. The imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific scene date, path, row and quadrants used as classification inputs for each state and year. 2011-01-01 2020-12-31 Source Contribution: Raw data used in land cover spectral signature analysis Source Type: online download Landsat TM/ETM/OLI/TIRS 2024 USGS, EROS Sioux Falls South Dakota 57198-001 United States Geological Survey (USGS), Earth Resources Observation and Science (EROS) The CDL has used Landsat satellite imagery as a primary input throughout the entire history of the program from 1997 to current. The Landsat data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific imagery and ancillary data used as classification inputs for each state and year. 1997-01-01 2024-12-31 Source Contribution: spatial and attribute information used in land cover spectral signature analysisSource Type: online 3D Elevation Program (3DEP) 2024-01-01 USGS, EROS Data Center Sioux Falls South Dakota 57198 USA United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center The 3D Elevation Program (3DEP) is used as an ancillary data source in the production of the Cropland Data Layer. More information can be found at <https://www.usgs.gov/3d-elevation-program>. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific imagery and ancillary data used as classification inputs for each state and year. Source Contribution: Raw data used in land cover spectral signature analysisSource Type: online National Land Cover Database 2019 (NLCD 2019) 2021 USGS, EROS Data Center Sioux Falls South Dakota 57198 USA United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center The NLCD is used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD Imperviousness Layer is used as an ancillary input to improve the classification. The Tree Canopy Layer was used as ancillary data sources in past CDL classifications. Refer to <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> for specific imagery and ancillary data used as classification inputs for each state and year. Source Contribution: spatial and attribute information used in the spectral signature training and validation of agricultural land coverSource Type: online 4800 USDA, FSA Common Land Unit (CLU) 2024 USDA, FSA Aerial Photography Field Office Salt Lake City Utah 84119-2020 USA United States Department of Agriculture (USDA), Farm Service Agency (FSA) Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>. 2024-10-01 OVERVIEW: NEW 10-METER 2024 CDL: Traditionally the CDL has been a 30-meter product for the years 1997-2023. Beginning in 2024, the CDL is a 10-meter product. The 2024 CDL crop classification utilized remote sensing data from harmonized Sentinel-2 MSI Level-2A, Landsat 8, and Landsat 9 Level-2 Collection 2 Tier-1 products, providing surface reflectance (SR) data across multiple spectral bands, including GREEN, RED, NIR, SWIR1, SWIR2, and RedEdge bands 1-4. To mitigate cloud cover, 10-day median composites of surface reflectance and NDVI were created from the cloud-masked Landsat-Sentinel multi-sensor data for the growing season of 2024. An impervious layer from USGS NLCD 2021 and a digital elevation model from USGS 3DEP were also included ancillary input variables. In addition, mixed sampling strategies and localized training and were applied to the 2024 10m CDL production. Additional information: Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079. PDF available at <https://www.nass.usda.gov/Research_and_Science/Cropland/docs/IGARSS2024_Proceedings_10mCDL_Li_etal.pdf>. FOR MORE TECHNICAL DETAILS AND PROGRAM HISTORY: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php> The United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal CDL acreage estimates, which most closely aligned with planted acres, are not simple pixel counting but regression estimates using NASS survey data. It is more of an 'Adjusted Census by Satellite.' SOFTWARE: New for the 2024 CDL a random forest classifier in Google Earth Engine was used to create the classification. ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. RANDOM FOREST CLASSIFIER: The 2024 Cropland Data Layer uses a random forest classifier approach. This is a departure from previous CDLs (2008-2023) that used a decision tree classifier using See5 software. Older CDLs (pre-2007) had limited ground reference training and less satellite imagery inputs and used a maximum likelihood classifier approach. GROUND TRUTH: As with the maximum likelihood method and decision tree classifiers, random forest is a supervised classification technique. Thus, it relies on having a sample of known ground reference areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground reference from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground reference provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <https://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data. INPUTS: The 2024 CDL has a spatial resolution of 10 meters and was produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected throughout the growing season. Additional ancillary inputs were used to supplement and improve the land cover classification including the United States Geological Survey (USGS) 3D Elevation Program (3DEP) data and the USGS National Land Cover Database imperviousness data. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The USGS NLCD is used as non-agricultural training and validation data. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view complete lists of imagery, ancillary inputs and training and validation used for a specific state and year. ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view or download full accuracy reports by state and year. PUBLIC RELEASE: The USDA NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer. 2024-12-31T00:00:00 USDA, NASS, Spatial Analysis Research Section 800-727-9540 855-493-0447 1400 Independence Avenue, SW, Room 5029 South Building Washington District of Columbia 20250-2001 USA HQ_RDD_GIB@usda.gov USDA, NASS, Spatial Analysis Research Section staff Metadata Access Constraints: No restrictions on the distribution or use of the metadata file Metadata Use Constraints: No restrictions on the distribution or use of the metadata file This metadata was automatically generated from the FGDC Content Standards for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2015-07-24 version of the FGDC RSE to ISO 19115-2 transform. Translated from FGDC 2021-02-06T13:48:43.052-05:00 Updated for the 2024 annual release 2025-03-04T11:50:00-05:00 USDA, NASS, Spatial Analysis Research Section 800-727-9540 855-493-0447 1400 Independence Avenue, SW, Room 5029 South Building Washington District of Columbia 20250-2001 USA HQ_RDD_GIB@usda.gov USDA, NASS, Spatial Analysis Research Section Staff