2023 Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

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

Publication_Date: 20240131
Title: 2023 Cropland Data Layer
Edition: 2023 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:

NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <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/>.

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 2023 CDL has a spatial resolution of 30 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. Some CDL states used additional ancillary inputs to supplement and improve the land cover classification including historical CDL data, the United States Geological Survey (USGS) National Elevation Dataset (NED), USDA National Resources Conservation Service (NRCS) National Commodity Crop Productivity Index (NCCPI), and the most current versions of the USGS National Land Cover Database imperviousness and the 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, Oregon State University tree crop and vineyard data, Utah Department of Water Resources, and Washington State Department of Agriculture. The most current version of the 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 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: 20221001
Ending_Date: 20231231

Currentness_Reference: 2023 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: annual updates

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -127.8873
East_Bounding_Coordinate: -74.1585
North_Bounding_Coordinate: 47.9580
South_Bounding_Coordinate: 23.1496

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: 2023

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 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <https://www.mrlc.gov/>.

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. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions (pre-2007) of the CDL that 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 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, 2023 Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only             *Correct  Accuracy       Error      Kappa
-------------------------              -------   --------     ------      -----
FSA Crops                           16,630,392      81.6%      18.4%      0.788

Cover                     Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                           Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                           ----     ------   --------      -----      -----   --------      -----      -----
Corn                              1  4,756,830      90.5%       9.5%      0.891      93.0%       7.0%      0.919
Cotton                            2    751,873      86.4%      13.6%      0.861      86.2%      13.8%      0.859
Rice                              3    291,151      93.6%       6.4%      0.936      97.4%       2.6%      0.974
Sorghum                           4    253,934      67.7%      32.3%      0.674      73.3%      26.7%      0.731
Soybeans                          5  4,068,090      90.0%      10.0%      0.888      91.8%       8.2%      0.908
Sunflower                         6     39,961      73.9%      26.1%      0.738      86.9%      13.1%      0.868
Peanuts                          10    233,305      80.7%      19.3%      0.806      88.1%      11.9%      0.880
Tobacco                          11      3,651      57.1%      42.9%      0.571      79.7%      20.3%      0.797
Sweet Corn                       12     12,806      60.2%      39.8%      0.602      79.6%      20.4%      0.796
Pop or Orn Corn                  13      8,934      65.0%      35.0%      0.650      88.5%      11.5%      0.885
Mint                             14      4,243      66.3%      33.7%      0.663      86.0%      14.0%      0.860
Barley                           21    129,067      68.2%      31.8%      0.681      77.8%      22.2%      0.777
Durum Wheat                      22     50,829      72.1%      27.9%      0.721      79.1%      20.9%      0.790
Spring Wheat                     23    394,374      86.2%      13.8%      0.861      82.7%      17.3%      0.825
Winter Wheat                     24  1,231,250      85.3%      14.7%      0.847      85.4%      14.6%      0.848
Other Small Grains               25        254      39.4%      60.6%      0.394      67.9%      32.1%      0.679
Dbl Crop WinWht/Soybeans         26    390,071      83.2%      16.8%      0.830      84.6%      15.4%      0.844
Rye                              27     19,261      38.5%      61.5%      0.385      62.2%      37.8%      0.621
Oats                             28     41,650      40.2%      59.8%      0.401      63.2%      36.8%      0.631
Millet                           29     32,972      54.2%      45.8%      0.541      71.0%      29.0%      0.710
Speltz                           30         76      13.3%      86.7%      0.133      42.7%      57.3%      0.427
Canola                           31     77,955      85.9%      14.1%      0.859      93.0%       7.0%      0.929
Flaxseed                         32      2,239      36.5%      63.5%      0.365      72.2%      27.8%      0.722
Safflower                        33      8,177      54.9%      45.1%      0.549      71.9%      28.1%      0.719
Rape Seed                        34         39      23.5%      76.5%      0.235      61.9%      38.1%      0.619
Mustard                          35      6,706      59.2%      40.8%      0.592      82.2%      17.8%      0.822
Alfalfa                          36  1,026,510      81.8%      18.2%      0.812      82.4%      17.6%      0.819
Other Hay/Non Alfalfa            37    901,122      59.4%      40.6%      0.580      70.3%      29.7%      0.691
Camelina                         38      1,035      37.6%      62.4%      0.376      72.8%      27.2%      0.728
Buckwheat                        39        629      36.3%      63.7%      0.363      73.1%      26.9%      0.731
Sugarbeets                       41     60,850      88.0%      12.0%      0.880      94.1%       5.9%      0.941
Dry Beans                        42     52,273      72.7%      27.3%      0.726      84.1%      15.9%      0.841
Potatoes                         43     86,943      85.9%      14.1%      0.859      89.5%      10.5%      0.894
Other Crops                      44      3,158      30.1%      69.9%      0.301      69.7%      30.3%      0.697
Sugarcane                        45    136,592      96.0%       4.0%      0.959      96.5%       3.5%      0.965
Sweet Potatoes                   46      7,904      70.2%      29.8%      0.701      92.3%       7.7%      0.923
Misc Vegs & Fruits               47         54       7.1%      92.9%      0.071      16.6%      83.4%      0.166
Watermelons                      48      1,675      41.8%      58.2%      0.418      69.8%      30.2%      0.698
Onions                           49     11,134      67.9%      32.1%      0.679      79.5%      20.5%      0.795
Cucumbers                        50      1,560      54.5%      45.5%      0.545      78.4%      21.6%      0.784
Chick Peas                       51     21,919      79.8%      20.2%      0.798      86.3%      13.7%      0.863
Lentils                          52     19,795      70.7%      29.3%      0.707      81.5%      18.5%      0.815
Peas                             53     40,952      70.8%      29.2%      0.708      83.4%      16.6%      0.834
Tomatoes                         54     22,315      85.6%      14.4%      0.856      84.8%      15.2%      0.848
Caneberries                      55        618      77.4%      22.6%      0.774      71.9%      28.1%      0.719
Hops                             56      5,731      92.3%       7.7%      0.923      93.8%       6.2%      0.938
Herbs                            57      2,494      33.2%      66.8%      0.332      69.7%      30.3%      0.697
Clover/Wildflowers               58      7,248      51.7%      48.3%      0.516      74.4%      25.6%      0.743
Sod/Grass Seed                   59     71,502      75.5%      24.5%      0.754      82.5%      17.5%      0.825
Switchgrass                      60        349      49.6%      50.4%      0.496      66.7%      33.3%      0.667
Fallow/Idle Cropland             61    705,515      77.6%      22.4%      0.771      85.9%      14.1%      0.856
Cherries                         66      6,248      77.4%      22.6%      0.774      81.7%      18.3%      0.817
Peaches                          67      3,174      63.9%      36.1%      0.639      77.2%      22.8%      0.772
Apples                           68     20,231      82.8%      17.2%      0.828      85.5%      14.5%      0.855
Grapes                           69     33,787      90.2%       9.8%      0.902      92.5%       7.5%      0.925
Christmas Trees                  70      1,153      37.4%      62.6%      0.374      61.2%      38.8%      0.612
Other Tree Crops                 71      6,667      76.2%      23.8%      0.762      76.8%      23.2%      0.768
Citrus                           72     19,216      88.9%      11.1%      0.889      86.8%      13.2%      0.868
Pecans                           74     48,609      82.3%      17.7%      0.822      94.0%       6.0%      0.940
Almonds                          75     57,934      92.0%       8.0%      0.920      90.6%       9.4%      0.906
Walnuts                          76     14,991      89.2%      10.8%      0.892      89.5%      10.5%      0.895
Pears                            77      2,098      75.8%      24.2%      0.758      82.6%      17.4%      0.826
Aquaculture                      92     48,991      90.9%       9.1%      0.909      92.4%       7.6%      0.924
Open Water                      111    492,266      92.1%       7.9%      0.920      92.0%       8.0%      0.919
Perennial Ice/Snow              112      1,448      56.5%      43.5%      0.565      73.7%      26.3%      0.737
Developed/Open Space            121    839,810      97.9%       2.1%      0.978      76.6%      23.4%      0.760
Developed/Low Intensity         122    555,783      99.3%       0.7%      0.993      85.3%      14.7%      0.851
Developed/Med Intensity         123    315,518      99.7%       0.3%      0.997      92.0%       8.0%      0.919
Developed/High Intensity        124    117,434      99.9%       0.1%      0.999      97.5%       2.5%      0.975
Barren                          131    164,159      81.3%      18.7%      0.812      86.0%      14.0%      0.859
Deciduous Forest                141  2,904,050      86.8%      13.2%      0.855      79.3%      20.7%      0.774
Evergreen Forest                142  2,259,390      84.2%      15.8%      0.830      80.0%      20.0%      0.785
Mixed Forest                    143    528,086      48.3%      51.7%      0.471      56.2%      43.8%      0.550
Shrubland                       152  3,675,410      89.7%      10.3%      0.885      89.8%      10.2%      0.887
Grassland/Pasture               176  3,068,770      81.9%      18.1%      0.798      75.2%      24.8%      0.726
Woody Wetlands                  190  1,024,430      71.4%      28.6%      0.703      71.5%      28.5%      0.705
Herbaceous Wetlands             195    248,567      59.9%      40.1%      0.596      68.2%      31.8%      0.678
Pistachios                      204     25,326      90.9%       9.1%      0.909      88.0%      12.0%      0.880
Triticale                       205     25,662      33.0%      67.0%      0.330      58.0%      42.0%      0.580
Carrots                         206      1,675      49.7%      50.3%      0.497      56.1%      43.9%      0.561
Asparagus                       207          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Garlic                          208      1,492      63.1%      36.9%      0.631      66.0%      34.0%      0.660
Cantaloupes                     209        412      34.9%      65.1%      0.349      43.7%      56.3%      0.437
Prunes                          210          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Olives                          211      1,765      78.8%      21.2%      0.788      84.7%      15.3%      0.847
Oranges                         212    243,040      98.7%       1.3%      0.987      98.4%       1.6%      0.984
Honeydew Melons                 213         47      30.5%      69.5%      0.305      30.1%      69.9%      0.301
Broccoli                        214        810      44.0%      56.0%      0.440      36.4%      63.6%      0.364
Avocados                        215         78      63.9%      36.1%      0.639      44.1%      55.9%      0.441
Peppers                         216      1,034      41.0%      59.0%      0.410      60.6%      39.4%      0.606
Pomegranates                    217      1,583      95.0%       5.0%      0.950      87.1%      12.9%      0.871
Nectarines                      218         20      16.4%      83.6%      0.164      50.0%      50.0%      0.500
Greens                          219      1,108      43.5%      56.5%      0.434      36.1%      63.9%      0.361
Plums                           220        137      11.5%      88.5%      0.115      45.1%      54.9%      0.451
Strawberries                    221        555      65.6%      34.4%      0.656      79.4%      20.6%      0.794
Squash                          222        487      26.2%      73.8%      0.262      54.6%      45.4%      0.546
Apricots                        223         14      34.1%      65.9%      0.341       7.5%      92.5%      0.075
Vetch                           224         43      11.5%      88.5%      0.115      76.8%      23.2%      0.768
Dbl Crop WinWht/Corn            225     21,052      50.7%      49.3%      0.507      64.0%      36.0%      0.639
Dbl Crop Oats/Corn              226      7,245      62.4%      37.6%      0.623      70.0%      30.0%      0.700
Lettuce                         227      2,344      42.7%      57.3%      0.427      44.4%      55.6%      0.444
Dbl Crop Triticale/Corn         228     24,193      56.3%      43.7%      0.562      71.2%      28.8%      0.711
Pumpkins                        229      1,672      35.2%      64.8%      0.352      79.4%      20.6%      0.794
Dbl Crop Lettuce/Cantaloupe     231        954      72.2%      27.8%      0.722      62.6%      37.4%      0.626
Dbl Crop Lettuce/Cotton         232      1,634      64.5%      35.5%      0.645      73.8%      26.2%      0.738
Dbl Crop Lettuce/Barley         233        224      68.5%      31.5%      0.685      28.3%      71.7%      0.283
Dbl Crop Barley/Sorghum         235          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Dbl Crop WinWht/Sorghum         236     17,967      37.9%      62.1%      0.378      61.0%      39.0%      0.609
Dbl Crop Barley/Corn            237      4,282      55.7%      44.3%      0.557      65.9%      34.1%      0.659
Dbl Crop WinWht/Cotton          238      6,486      32.1%      67.9%      0.321      62.3%      37.7%      0.623
Dbl Crop Soybeans/Cotton        239          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop Soybeans/Oats          240      1,637      24.5%      75.5%      0.245      63.8%      36.2%      0.638
Dbl Crop Corn/Soybeans          241        882      54.5%      45.5%      0.545      92.2%       7.8%      0.922
Blueberries                     242     10,579      77.1%      22.9%      0.771      83.9%      16.1%      0.839
Cabbage                         243      1,640      52.8%      47.2%      0.528      69.8%      30.2%      0.698
Cauliflower                     244         72      10.1%      89.9%      0.101      23.4%      76.6%      0.234
Celery                          245         98      24.9%      75.1%      0.249      34.9%      65.1%      0.349
Radishes                        246         80      12.1%      87.9%      0.121      65.6%      34.4%      0.656
Turnips                         247         99      31.8%      68.2%      0.318      54.7%      45.3%      0.547
Eggplants                       248          4      13.3%      86.7%      0.133      23.5%      76.5%      0.235
Gourds                          249         28      29.2%      70.8%      0.292      66.7%      33.3%      0.667
Cranberries                     250        130      52.8%      47.2%      0.528      79.8%      20.2%      0.798
Dbl Crop Barley/Soybeans        254      8,119      58.7%      41.3%      0.587      80.9%      19.1%      0.809

*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 2023 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 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://scihub.copernicus.eu/> 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: 2023
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. For the 2023 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

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: 20221001
Ending_Date: 20231231

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: 2023
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: 20221001
Ending_Date: 20231231

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) Earth Resources Observation and Science (EROS) Data Center

Publication_Date: 2009
Title: The National Elevation Dataset (NED)
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 USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. 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: NED
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: 2021
Title: National Land Cover Database 2019 (NLCD 2019)
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 2019 land cover was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2019 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The NLCD 2016 Tree Canopy data was used as an ancillary input. 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: 2023
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: 2023

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:

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

Publication_Date: 2012
Title: National Commodity Crop Productivity Index (NCCPI) Version 2.0
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Lincoln, Nebraska USA
Publisher:

United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center

Other_Citation_Details:

(Michigan only dataset) The NCCPI was used as an ancillary input for the Michigan CDL. The data was resampled to 30 meters for use in CDL production. For more information about the NCCPI: <https://www.nrcs.usda.gov/>.

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: NCCPI
Source_Contribution: Ancillary input used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator: California Department of Water Resources (DWR)
Publication_Date: 2023
Title: Statewide Land Use 2021 (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: 2021

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: 2023
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: 2023

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: 2023
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: 2023

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: 2023
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: 2023
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: 2023

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:

Gordon B. Jones, PhD, and Rick Hilton of Oregon State University; Karim Naguib of the Jackson County GIS Office

Publication_Date: unknown
Title: Pear and Vineyard Data for Jackson County, Oregon
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Central Point, Oregon 97502 USA
Publisher: unpublished

Other_Citation_Details:

(Oregon only dataset) The Oregon State University Pear and Vineyard Data for Jackson County, Oregon provides additional tree crop and vineyard training and validation data. Contact Gordon B. Jones at Oregon State University for more information.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2018

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: none
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: 2023
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: 2023

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: 2023
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: 2023

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: 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: 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. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine.

DECISION TREE CLASSIFIER: This Cropland Data Layer uses a decision tree classifier approach. Using a decision tree classifier is a departure from older versions (pre-2007) of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships.

GROUND TRUTH: As with the maximum likelihood method, decision tree analysis 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 2023 CDL has a spatial resolution of 30 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. Some CDL states used additional ancillary inputs to supplement and improve the land cover classification including the United States Geological Survey (USGS) National Elevation Dataset (NED) and the most current versions of the USGS National Land Cover Database imperviousness and the tree canopy data layers. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the 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: 2023
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

2023 Cropland Data Layer

Metadata: