2025 Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)

Publication_Date: 20260227
Title: 2025 Cropland Data Layer
Edition: 2025 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

USDA NASS Marketing and Information Services Office, Washington, D.C.

Publisher: USDA NASS

Other_Citation_Details:

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>. 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>.

Online_Linkage: <https://croplandcros.scinet.usda.gov/>

Description:

Abstract:

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.

Purpose:

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.

Supplemental_Information:

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>.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20241001
Ending_Date: 20251231

Currentness_Reference: 2025 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: annual updates

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -129.335318
East_Bounding_Coordinate: -73.226636
North_Bounding_Coordinate: 49.041442
South_Bounding_Coordinate: 21.809104

Keywords:

Theme:

Theme_Keyword_Thesaurus: NGDA Portfolio Themes
Theme_Keyword: National Geospatial Data Asset
Theme_Keyword: Land Use Land Cover Theme
Theme_Keyword: NGDA
Theme_Keyword: NGDA109

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

Theme:

Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: farming
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CroplandCROS

Place:

Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword: Continent > North America > United States of America

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: USA
Place_Keyword: CONUS

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2025

Access_Constraints: none
Use_Constraints:

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.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA NASS, Spatial Analysis Research Section
Contact_Person: USDA NASS, Spatial Analysis Research Section staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Data_Set_Credit: USDA National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft 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.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

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/>.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories.

Attribute_Accuracy_Explanation:

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.

Logical_Consistency_Report:

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.

Completeness_Report: The 2025 CDL covers the Continental United States.
Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

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.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: European Space Agency (ESA)
Publication_Date: 2025
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: European Commission, Brussels (Belgium)
Publisher: Copernicus - European Commission

Other_Citation_Details:

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/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20241001
Ending_Date: 20251231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: SENTINEL-2
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS)

Publication_Date: 2025
Title:

Landsat 8 and 9 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: Sioux Falls, South Dakota 57198-001
Publisher: USGS, EROS

Other_Citation_Details:

The 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.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20241001
Ending_Date: 20251231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat 8 and Landsat 9
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) National Geospatial Program

Publication_Date: 2025
Title: 3D Elevation Program (3DEP)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS

Other_Citation_Details:

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 the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.

Source_Scale_Denominator: 24000
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: 3DEP
Source_Contribution:

spatial and attribute information used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Publication_Date: 2024
Title: National Land Cover Database 2024 (NLCD 2024)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center

Other_Citation_Details:

The 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.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA) Farm Service Agency (FSA)

Publication_Date: 2025
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office

Other_Citation_Details:

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/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: FSA CLU
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: California Department of Water Resources (DWR)
Publication_Date: 2025
Title: Statewide Land Use 2023 (Provisional)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Sacramento, California 94236-0001 USA
Publisher: California Department of Water Resources (DWR)

Other_Citation_Details:

(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/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2023

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: LandIQ
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Publication_Date: 2025
Title:

Lower Colorado River Water Accounting System (LCRAS) GIS data layer

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Boulder City, NV 89006-1470, USA
Publisher:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Other_Citation_Details:

(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/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: LCRAS GIS Data
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)

Publication_Date: 2025
Title: USDA NASS Citrus Grove Data Layer
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Maitland, Florida 32751-7057 USA
Publisher: USDA NASS Florida Field Office

Other_Citation_Details:

(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.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: NASS Citrus Grove Data Layer
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: Florida Department of Agriculture and Consumer Services
Publication_Date: 2020
Title:

Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Tallahassee, Florida 32399-0800 USA
Publisher: Florida Department of Agriculture and Consumer Services

Other_Citation_Details:

(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>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2020

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: FSAID
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: Cornell Cooperative Extension, Lake Erie Regional Grape Program
Publication_Date: 2025
Title: GIS Mapping of Lake Erie Vineyards
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Portland, NY, 14769 USA
Publisher: Lake Erie Regional Grape Program at CLEREL - Cornell University

Other_Citation_Details:

(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/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Lake Erie Vineyards GIS data
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: Utah Division of Water Resources
Publication_Date: 2025
Title: Agriculture Check Polygons
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Salt Lake City, Utah 84116 USA
Publisher: Utah Division of Water Resources

Other_Citation_Details:

(Utah only dataset) The Utah Division of Water Resources Agriculture Check Polygon data provides additional training and validation data for Utah's cropland.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: Utah DWR Agriculture Check Polygons
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: Washington State Department of Agriculture (WSDA)
Publication_Date: 2025
Title: WSDA Crop Geodatabase
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Olympia, WA 98504-2560 USA
Publisher: Washington State Department of Agriculture

Other_Citation_Details:

(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/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2025

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: WSDA Crop Geodatabase
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Process_Step:

Process_Description:

OVERVIEW: 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/> 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.

Process_Date: 2025
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA NASS, Spatial Analysis Research Section
Contact_Person: USDA NASS, Spatial Analysis Research Section staff

Contact_Address:

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Cloud_Cover: 0

2025 Cropland Data Layer

Metadata: