United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)202602272025 Editionraster digital dataUSDA NASS Marketing and Information Services Office, Washington, D.C.USDA NASSZ. 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>. Data available free for download at <https://croplandcros.scinet.usda.gov/>. Frequently Asked Questions at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.<https://croplandcros.scinet.usda.gov/>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. The 10-meter 2025 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 2025 growing season. Ancillary inputs included the USGS NLCD 2024 Imperviousness Layer, the USGS NLCD 2023 Tree Canopy Layer, and the USGS 3DEP digital elevation model. In addition, mixed sampling strategies and localized training were applied to the 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 2025 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 and Tree Canopy data layers. 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 2024 NLCD was used as non-agricultural training and validation data for the 2025 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 supplemental acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.The data is available free for download through CroplandCROS at <https://croplandcros.scinet.usda.gov/>. Metadata, Frequently Asked Questions (FAQs), and the most current year of data is available free for download at the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.20241001202512312025 growing seasonCompleteannual updates-130.2328-63.672252.851021.7423NGDA Portfolio ThemesNational Geospatial Data AssetLand Use Land Cover ThemeNGDANGDAID109ISO 19115 Topic Categoryfarming, 001environment, 007imageryBaseMapsEarthCover, 010Global Change Master Directory (GCMD) Science KeywordsEarth Science > Biosphere > Terrestrial Ecosystems > Agricultural LandsEarth Science > Land Surface > Land Use/Land Cover > Land CoverNonecrop covercroplandagriculturefarmingland covercrop estimatesESA SENTINEL-2LandsatCroplandCROSGlobal Change Master Directory (GCMD) Location KeywordsContinent > North America > United States of AmericaNoneUnited StatesUSACONUSNone2025noneThe 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.USDA NASS, Spatial Analysis Research SectionUSDA NASS, Spatial Analysis Research Section staffmailing and physical address

1400 Independence Avenue, SW, Room 5029 South Building

WashingtonDistrict of Columbia20250-2001USA800-727-9540855-493-0447SM.NASS.RDD.GIB@usda.govUSDA National Agricultural Statistics ServiceNoneUnclassifiedNoneMicrosoft Windows 10 and 11 Enterprise; Google Earth Engine <https://earthengine.google.com/>; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Pro 3.4.4 <https://www.esri.com/>. Beginning in 2024, the CDL uses 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 at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to verify the methodology used for a specific state and year.If the following table does not display properly, then please visit the CDL Metadata webpage at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original file. Accuracy at the individual state-level can be viewed at the CDL Metadata webpage. >USDA National Agricultural Statistics Service, 2025 Cropland Data Layer >STATEWIDE AGRICULTURAL ACCURACY REPORT > >Crop-specific covers only *Correct Accuracy Error Kappa >------------------------- ------- -------- ------ ----- >OVERALL ACCURACY** 2,631,177 75.43% 24.57% 0.712 > >Cover Attribute *Correct Producer's Omission User's Commission Cond'l >Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa >---- ---- ------ -------- ----- ----- -------- ----- ----- >Corn 1 921,280 92.2% 7.8% 0.918 91.2% 8.8% 0.908 >Cotton 2 94,024 85.0% 15.0% 0.850 86.5% 13.5% 0.864 >Rice 3 34,986 95.7% 4.4% 0.956 91.4% 8.6% 0.914 >Sorghum 4 69,213 76.4% 23.6% 0.763 78.7% 21.3% 0.786 >Soybeans 5 696,385 91.0% 9.0% 0.907 89.6% 10.4% 0.892 >Sunflower 6 11,614 79.2% 20.8% 0.792 87.8% 12.2% 0.878 >Peanuts 10 22,174 89.5% 10.6% 0.894 78.6% 21.4% 0.785 >Tobacco 11 402 31.6% 68.5% 0.316 79.8% 20.2% 0.798 >Sweet Corn 12 1,211 49.7% 50.3% 0.497 74.9% 25.1% 0.749 >Pop or Orn Corn 13 2,041 70.2% 29.8% 0.702 79.9% 20.1% 0.799 >Mint 14 465 74.8% 25.2% 0.748 85.0% 15.0% 0.850 >Barley 21 16,542 67.7% 32.3% 0.677 70.9% 29.1% 0.709 >Durum Wheat 22 19,821 74.2% 25.8% 0.742 75.8% 24.2% 0.758 >Spring Wheat 23 86,527 84.9% 15.1% 0.848 83.1% 16.9% 0.830 >Winter Wheat 24 206,712 85.5% 14.5% 0.853 82.6% 17.4% 0.824 >Other Small Grains 25 83 33.6% 66.4% 0.336 77.6% 22.4% 0.776 >Dbl Crop WinWht/Soybeans 26 32,668 75.0% 25.0% 0.749 83.0% 17.0% 0.830 >Rye 27 3,714 41.3% 58.7% 0.413 53.7% 46.3% 0.536 >Oats 28 10,890 46.6% 53.4% 0.465 59.3% 40.7% 0.592 >Millet 29 5,949 54.4% 45.6% 0.544 71.2% 28.8% 0.712 >Speltz 30 51 22.9% 77.1% 0.229 62.2% 37.8% 0.622 >Canola 31 23,207 92.4% 7.6% 0.924 90.8% 9.2% 0.908 >Flaxseed 32 2,137 54.3% 45.7% 0.543 79.2% 20.8% 0.792 >Safflower 33 1,412 69.6% 30.4% 0.696 82.1% 17.9% 0.821 >Rape Seed 34 9 18.0% 82.0% 0.180 50.0% 50.0% 0.500 >Mustard 35 1,335 80.4% 19.6% 0.804 81.2% 18.8% 0.812 >Alfalfa 36 89,730 67.3% 32.8% 0.670 67.7% 32.3% 0.675 >Other Hay/Non Alfalfa 37 40,327 32.1% 67.9% 0.316 25.8% 74.2% 0.254 >Camelina 38 360 47.9% 52.1% 0.479 58.3% 41.7% 0.583 >Buckwheat 39 136 47.6% 52.5% 0.476 81.0% 19.0% 0.810 >Sugarbeets 41 7,232 91.9% 8.1% 0.919 90.9% 9.1% 0.909 >Dry Beans 42 11,047 78.4% 21.6% 0.784 84.0% 16.0% 0.840 >Potatoes 43 7,893 86.8% 13.2% 0.868 88.8% 11.2% 0.888 >Other Crops 44 646 35.7% 64.3% 0.357 60.5% 39.5% 0.605 >Sugarcane 45 13,706 92.6% 7.4% 0.926 91.1% 8.9% 0.911 >Sweet Potatoes 46 1,013 76.3% 23.7% 0.763 82.9% 17.1% 0.829 >Misc Vegs & Fruits 47 108 13.0% 87.0% 0.130 51.7% 48.3% 0.517 >Watermelons 48 341 41.6% 58.4% 0.416 66.2% 33.8% 0.662 >Onions 49 942 70.8% 29.2% 0.708 80.9% 19.1% 0.809 >Cucumbers 50 273 58.6% 41.4% 0.586 69.6% 30.4% 0.696 >Chick Peas 51 6,065 81.4% 18.7% 0.813 85.7% 14.3% 0.857 >Lentils 52 10,572 82.1% 17.9% 0.821 79.7% 20.3% 0.797 >Peas 53 13,067 78.9% 21.1% 0.789 79.2% 20.8% 0.792 >Tomatoes 54 2,231 83.0% 17.0% 0.830 87.9% 12.1% 0.879 >Caneberries 55 61 50.4% 49.6% 0.504 77.2% 22.8% 0.772 >Hops 56 479 82.4% 17.6% 0.824 77.5% 22.5% 0.775 >Herbs 57 914 49.4% 50.6% 0.494 67.0% 33.0% 0.670 >Clover/Wildflowers 58 746 30.5% 69.5% 0.305 53.0% 47.0% 0.530 >Sod/Grass Seed 59 4,664 48.0% 52.0% 0.480 62.8% 37.2% 0.628 >Switchgrass 60 44 6.6% 93.4% 0.066 36.7% 63.3% 0.367 >Fallow/Idle Cropland 61 73,869 76.1% 23.9% 0.760 73.4% 26.6% 0.733 >Shrubland 64 27,145 59.2% 40.8% 0.591 65.0% 35.0% 0.650 >Cherries 66 1,082 45.9% 54.1% 0.459 64.6% 35.4% 0.646 >Peaches 67 991 44.3% 55.7% 0.443 66.4% 33.6% 0.664 >Apples 68 2,324 55.1% 44.9% 0.551 72.6% 27.4% 0.726 >Grapes 69 11,460 70.6% 29.4% 0.706 78.3% 21.7% 0.783 >Christmas Trees 70 207 11.7% 88.3% 0.117 48.8% 51.2% 0.488 >Other Tree Crops 71 462 33.9% 66.2% 0.338 68.9% 31.1% 0.689 >Citrus 72 4,661 75.1% 24.9% 0.750 74.9% 25.1% 0.749 >Pecans 74 3,883 49.4% 50.6% 0.494 67.1% 32.9% 0.670 >Almonds 75 19,748 93.1% 6.9% 0.931 81.9% 18.1% 0.819 >Walnuts 76 4,895 85.8% 14.2% 0.858 76.6% 23.4% 0.766 >Pears 77 294 37.8% 62.2% 0.378 67.4% 32.6% 0.674 >Aquaculture 92 4,179 79.0% 21.0% 0.790 82.7% 17.3% 0.827 >Pistachios 204 7,750 87.6% 12.4% 0.876 86.4% 13.6% 0.864 >Triticale 205 4,909 42.6% 57.4% 0.425 51.2% 48.8% 0.512 >Carrots 206 237 50.8% 49.3% 0.507 65.3% 34.7% 0.653 >Asparagus 207 3 16.7% 83.3% 0.167 50.0% 50.0% 0.500 >Garlic 208 250 74.2% 25.8% 0.742 85.6% 14.4% 0.856 >Cantaloupes 209 83 45.1% 54.9% 0.451 56.8% 43.2% 0.568 >Prunes 210 495 55.1% 44.9% 0.551 71.3% 28.7% 0.713 >Olives 211 624 38.8% 61.2% 0.388 70.7% 29.3% 0.707 >Oranges 212 2,232 74.4% 25.6% 0.744 53.4% 46.6% 0.534 >Honeydew Melons 213 16 28.6% 71.4% 0.286 76.2% 23.8% 0.762 >Broccoli 214 63 41.7% 58.3% 0.417 45.0% 55.0% 0.450 >Avocados 215 681 55.0% 45.0% 0.550 67.0% 33.0% 0.670 >Peppers 216 91 32.2% 67.8% 0.322 55.8% 44.2% 0.558 >Pomegranates 217 223 44.3% 55.7% 0.443 79.4% 20.6% 0.794 >Nectarines 218 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 >Greens 219 107 41.5% 58.5% 0.415 47.3% 52.7% 0.473 >Plums 220 134 30.1% 69.9% 0.301 39.5% 60.5% 0.395 >Strawberries 221 93 45.4% 54.6% 0.454 69.9% 30.1% 0.699 >Squash 222 59 22.6% 77.4% 0.226 52.2% 47.8% 0.522 >Apricots 223 43 18.1% 81.9% 0.181 47.8% 52.2% 0.478 >Vetch 224 91 55.2% 44.9% 0.552 81.3% 18.8% 0.812 >Dbl Crop WinWht/Corn 225 2,578 47.9% 52.1% 0.479 62.3% 37.7% 0.623 >Dbl Crop Oats/Corn 226 560 53.7% 46.3% 0.537 73.8% 26.2% 0.738 >Lettuce 227 203 59.0% 41.0% 0.590 49.9% 50.1% 0.499 >Dbl Crop Triticale/Corn 228 2,325 49.9% 50.1% 0.499 71.4% 28.6% 0.714 >Pumpkins 229 257 26.7% 73.3% 0.267 66.2% 33.8% 0.662 >Dbl Crop Lettuce/Cantaloupe 231 37 75.5% 24.5% 0.755 92.5% 7.5% 0.925 >Dbl Crop Lettuce/Cotton 232 102 89.5% 10.5% 0.895 92.7% 7.3% 0.927 >Dbl Crop WinWht/Sorghum 236 2,444 46.2% 53.8% 0.462 51.8% 48.2% 0.518 >Dbl Crop Barley/Corn 237 274 44.2% 55.8% 0.442 70.6% 29.4% 0.706 >Dbl Crop WinWht/Cotton 238 1,023 42.5% 57.6% 0.424 45.6% 54.4% 0.456 >Dbl Crop Soybeans/Oats 240 297 30.5% 69.5% 0.305 56.6% 43.4% 0.566 >Dbl Crop Corn/Soybeans 241 62 36.3% 63.7% 0.363 55.9% 44.1% 0.559 >Blueberries 242 761 35.3% 64.7% 0.353 67.0% 33.0% 0.670 >Cabbage 243 124 43.8% 56.2% 0.438 55.6% 44.4% 0.556 >Cauliflower 244 25 32.1% 68.0% 0.321 43.9% 56.1% 0.439 >Celery 245 13 31.0% 69.1% 0.310 52.0% 48.0% 0.520 >Radishes 246 48 42.5% 57.5% 0.425 73.8% 26.2% 0.738 >Turnips 247 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 >Eggplants 248 1 8.3% 91.7% 0.083 50.0% 50.0% 0.500 >Gourds 249 7 50.0% 50.0% 0.500 50.0% 50.0% 0.500 >Cranberries 250 29 16.2% 83.8% 0.162 90.6% 9.4% 0.906 >Dbl Crop Barley/Soybeans 254 498 46.1% 53.9% 0.461 74.2% 25.8% 0.742 > >*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix. >**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255). >FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy. The accuracy of the non-agricultural land cover classes within the Cropland Data Layer 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. 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.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.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 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 Continental United States unless noted otherwise in the 'Completeness Report' section of this metadata file.The 2025 CDL covers the Continental United States.The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 and 9 OLI/TIRS imagery uses the Collection 2 Level-1 specifications. Please reference the metadata on the USGS Glovis website for the positional accuracy of each Landsat scene. The Sentinel 2A and 2B imagery uses using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please reference the metadata on the Copernicus website for positional accuracy details.European Space Agency (ESA)2025remote-sensing imageEuropean Commission, Brussels (Belgium)Copernicus - European CommissionThe 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/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.10 meteronline download2024100120251231ground conditionSENTINEL-2Raw data used in land cover spectral signature analysisUnited States Geological Survey (USGS) Earth Resources Observation and Science (EROS)2025remote-sensing imageSioux Falls, South Dakota 57198-001USGS, EROSThe Landsat 8 and 9 OLI/TIRS 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 the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.30 meteronline download2024100120251231ground conditionLandsat 8 and Landsat 9Raw data used in land cover spectral signature analysisUnited States Geological Survey (USGS) National Geospatial Program2025remote-sensing imageSioux Falls, South Dakota 57198 USAUSGSThe 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 the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.24000onlineunknownground condition3DEPspatial and attribute information used in land cover spectral signature analysisUnited States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center2024remote-sensing imageSioux Falls, South Dakota 57198 USAUSGS, EROS Data CenterThe NLCD 2024 land cover was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2024 Imperviousness and 2023 Tree Canopy data layers were used as an ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.30 meteronlineunknownground conditionNLCDRaw data used in land cover spectral signature analysisUnited States Department of Agriculture (USDA) Farm Service Agency (FSA)2025vector digital dataSalt Lake City, Utah 84119-2020 USAUSDA, FSA Aerial Photography Field OfficeAccess 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/>.4800online2025ground condition, updated annuallyFSA CLUspatial and attribute information used in the spectral signature training and validation of agricultural land coverCalifornia Department of Water Resources (DWR)2025vector digital dataSacramento, California 94236-0001 USACalifornia Department of Water Resources (DWR)(California only dataset) The California Department of Water Resources Land Use Program data is used as additional crop-specific ground reference training and validation for tree crops and vineyards in California. More information about California Department of Water Resources Land Use Program can be found online at <https://data.cnra.ca.gov/dataset/statewide-crop-mapping> and <https://www.landiq.com/>.4800online2023ground conditionLandIQspatial and attribute information used in the spectral signature training and validation of agricultural land coverUnited States Department of Interior, Bureau of Reclamation, Lower Colorado Region2025vector digital dataBoulder City, NV 89006-1470, USAUnited States Department of Interior, Bureau of Reclamation, Lower Colorado Region(Arizona and California only dataset) The Lower Colorado River Water Accounting System (LCRAS) GIS data layer contains an annually updated record of crop types that was used to supplement the training and validation of the Cropland Data Layer. The area covered is Southern California and Southwest Arizona. For more details, please reference the Bureau of Reclamation website <https://www.usbr.gov/lc/>.4800online2025ground condition, updated annuallyLCRAS GIS Dataspatial and attribute information used in the spectral signature training and validation of agricultural land coverUnited States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)2025vector digital dataMaitland, Florida 32751-7057 USAUSDA NASS Florida Field Office(Florida only dataset) The Citrus Grove Data Layer is used as additional citrus training and validation ground reference data. Access to the USDA National Agricultural Statistics Service (NASS) Citrus Grove Data Layer is unpublished, for internal NASS use only.4800online2025ground condition, updated annuallyNASS Citrus Grove Data Layerspatial and attribute information used in the spectral signature training and validation of agricultural land coverFlorida Department of Agriculture and Consumer Services2020vector digital dataTallahassee, Florida 32399-0800 USAFlorida Department of Agriculture and Consumer Services(Florida only dataset) The Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase provides additional training and validation ground reference for Florida specialty tree crops. More information about this data set can be found online at <https://www.fdacs.gov/Agriculture-Industry/Water/Agricultural-Water-Supply-Planning>.4800online2020ground conditionFSAIDspatial and attribute information used in the spectral signature training and validation of agricultural land coverCornell Cooperative Extension, Lake Erie Regional Grape Program2025vector digital dataPortland, NY, 14769 USALake Erie Regional Grape Program at CLEREL - Cornell University(New York, Ohio and Pennsylvania only dataset) The Lake Erie Vineyards GIS data provides additional training and validation data for vineyards. More information can be found at <https://lergp.cce.cornell.edu/>.4800online2025ground conditionLake Erie Vineyards GIS dataspatial and attribute information used in the spectral signature training and validation of agricultural land coverUtah Division of Water Resources2025vector digital dataSalt Lake City, Utah 84116 USAUtah Division of Water Resources(Utah only dataset) The Utah Division of Water Resources Agriculture Check Polygon data provides additional training and validation data for Utah's cropland.4800online2025ground condition, updated annuallyUtah DWR Agriculture Check Polygonsspatial and attribute information used in the spectral signature training and validation of agricultural land coverWashington State Department of Agriculture (WSDA)2025vector digital dataOlympia, WA 98504-2560 USAWashington State Department of Agriculture(Washington only dataset) The WSDA Crop Geodatabase provides additional training and validation data for Washington's orchards, vineyards and small acreage crops. More information about the WSDA Crop Geodatabase can be found at <https://agr.wa.gov/>.4800online2025ground condition, updated annuallyWSDA Crop Geodatabasespatial and attribute information used in the spectral signature training and validation of agricultural land coverOVERVIEW: NEW 10-METER CDL: The 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 2025 growing season. Ancillary inputs included the USGS NLCD 2024 Imperviousness Layer, the USGS NLCD 2023 Tree Canopy Layer, and the USGS 3DEP digital elevation model. In addition, mixed sampling strategies and localized training were applied to the 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 2025 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 2025 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 2025 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/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.2025USDA NASS, Spatial Analysis Research SectionUSDA NASS, Spatial Analysis Research Section staffmailing and physical address

1400 Independence Avenue, SW, Room 5029 South Building

WashingtonDistrict of Columbia20250-2001USA800-727-9540855-493-0447SM.NASS.RDD.GIB@usda.gov0Continental United StatesRasterPixel316295480509Albers Conical Equal Area as used by mrlc.gov (NLCD)29.50000045.500000-96.00000023.0000000.0000000.000000row and column1010metersNorth American Datum of 1983Geodetic Reference System 806378137.000000298.257223563The 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.If the following table does not display properly, then please visit the following website to view the original metadata 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 > "62" Pasture/Grass > "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/SoybeansUSDA NASS Customer ServiceUSDA NASS Customer Service Staffmailing and physical address

1400 Independence Avenue, SW, Room 5038-S

WashingtonDistrict of Columbia20250-9410USA800-727-9540855-493-0447SM.NASS.RDD.GIB@usda.govPlease visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.2025 Cropland Data LayerDisclaimer: 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. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS maintains a Frequently Asked Questions (FAQ's) section at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.GEOTIFF2025GEOTIFF<https://croplandcros.scinet.usda.gov/>The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>.The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.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.The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.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.If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using CroplandCROS <https://croplandcros.scinet.usda.gov/>.20260227USDA NASS, Spatial Analysis Research SectionUSDA NASS, Spatial Analysis Research Section Staffmailing and physical address

1400 Independence Avenue, SW, Room 5029 South Building

WashingtonDistrict of Columbia20250-2001USA800-727-9540855-493-0447SM.NASS.RDD.GIB@usda.govFGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998No restrictions on the distribution or use of the metadata fileNo restrictions on the distribution or use of the metadata file