Online Links: Other_Citation_Details:NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at . The data is available free for download through CroplandCROS and the Geospatial Data Gateway .What geographic area does the data set cover?West_Bounding_Coordinate: -127.8459
East_Bounding_Coordinate: -67.0096
North_Bounding_Coordinate: 49.3253
South_Bounding_Coordinate: 24.3321
What does it look like?There are over 100 potential land cover categories. The legend above lists only a subset of available categories.Does the data set describe conditions during a particular time period? Beginning_Date: 1997
Ending_Date: annual, ongoing
Currentness_Reference: annual growing season
What is the general form of this data set?Geospatial_Data_Presentation_Form: raster digital data, typically Geotiff (.tif)
How does the data set represent geographic features?How are geographic features stored in the data set?
Indirect_Spatial_Reference: Continental United States
This is a Raster data set.It contains the following raster data types: Dimensions 96523 x 153811, type PixelWhat coordinate system is used to represent geographic features?
The map projection used is (EPSG:5070) Albers Conical Equal Area as used by mrlc.gov (NLCD).
Projection parameters:Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000
Planar coordinates are encoded using row and column
Abscissae (x-coordinates) are specified to the nearest 30
Ordinates (y-coordinates) are specified to the nearest 30
Planar coordinates are specified in meters
The horizontal datum used is North American Datum of 1983.
The ellipsoid used is Geodetic Reference System 80.
The semi-major axis of the ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257223563.
How does the data set describe geographic features?Entity_and_Attribute_Overview:The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.Entity_and_Attribute_Detail_Citation: Data Dictionary: USDA National Agricultural Statistics Service, Cropland Data Layer Source: USDA National Agricultural Statistics Service The following is a cross reference list of the categorization codes and land covers. Note that not all land cover categories listed below will appear in an individual state. Raster Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0 Categorization Code Land Cover "0" Background Raster Attribute Domain Values and Definitions: CROPS 1-60 Categorization Code Land Cover "1" Corn "2" Cotton "3" Rice "4" Sorghum "5" Soybeans "6" Sunflower "10" Peanuts "11" Tobacco "12" Sweet Corn "13" Pop or Orn Corn "14" Mint "21" Barley "22" Durum Wheat "23" Spring Wheat "24" Winter Wheat "25" Other Small Grains "26" Dbl Crop WinWht/Soybeans "27" Rye "28" Oats "29" Millet "30" Speltz "31" Canola "32" Flaxseed "33" Safflower "34" Rape Seed "35" Mustard "36" Alfalfa "37" Other Hay/Non Alfalfa "38" Camelina "39" Buckwheat "41" Sugarbeets "42" Dry Beans "43" Potatoes "44" Other Crops "45" Sugarcane "46" Sweet Potatoes "47" Misc Vegs & Fruits "48" Watermelons "49" Onions "50" Cucumbers "51" Chick Peas "52" Lentils "53" Peas "54" Tomatoes "55" Caneberries "56" Hops "57" Herbs "58" Clover/Wildflowers "59" Sod/Grass Seed "60" Switchgrass Raster Attribute Domain Values and Definitions: NON-CROP 61-65 Categorization Code Land Cover "61" Fallow/Idle Cropland "62" Pasture/Grass "63" Forest "64" Shrubland "65" Barren Raster Attribute Domain Values and Definitions: CROPS 66-80 Categorization Code Land Cover "66" Cherries "67" Peaches "68" Apples "69" Grapes "70" Christmas Trees "71" Other Tree Crops "72" Citrus "74" Pecans "75" Almonds "76" Walnuts "77" Pears Raster Attribute Domain Values and Definitions: OTHER 81-109 Categorization Code Land Cover "81" Clouds/No Data "82" Developed "83" Water "87" Wetlands "88" Nonag/Undefined "92" Aquaculture Raster Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195 Categorization Code Land Cover "111" Open Water "112" Perennial Ice/Snow "121" Developed/Open Space "122" Developed/Low Intensity "123" Developed/Med Intensity "124" Developed/High Intensity "131" Barren "141" Deciduous Forest "142" Evergreen Forest "143" Mixed Forest "152" Shrubland "176" Grassland/Pasture "190" Woody Wetlands "195" Herbaceous Wetlands Raster Attribute Domain Values and Definitions: CROPS 195-255 Categorization Code Land Cover "204" Pistachios "205" Triticale "206" Carrots "207" Asparagus "208" Garlic "209" Cantaloupes "210" Prunes "211" Olives "212" Oranges "213" Honeydew Melons "214" Broccoli "215" Avocados "216" Peppers "217" Pomegranates "218" Nectarines "219" Greens "220" Plums "221" Strawberries "222" Squash "223" Apricots "224" Vetch "225" Dbl Crop WinWht/Corn "226" Dbl Crop Oats/Corn "227" Lettuce "228" Dbl Crop Triticale/Corn "229" Pumpkins "230" Dbl Crop Lettuce/Durum Wht "231" Dbl Crop Lettuce/Cantaloupe "232" Dbl Crop Lettuce/Cotton "233" Dbl Crop Lettuce/Barley "234" Dbl Crop Durum Wht/Sorghum "235" Dbl Crop Barley/Sorghum "236" Dbl Crop WinWht/Sorghum "237" Dbl Crop Barley/Corn "238" Dbl Crop WinWht/Cotton "239" Dbl Crop Soybeans/Cotton "240" Dbl Crop Soybeans/Oats "241" Dbl Crop Corn/Soybeans "242" Blueberries "243" Cabbage "244" Cauliflower "245" Celery "246" Radishes "247" Turnips "248" Eggplants "249" Gourds "250" Cranberries "254" Dbl Crop Barley/SoybeansWho produced the data set?Who are the originators of the data set?United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)Who also contributed to the data set?USDA National Agricultural Statistics ServiceTo whom should users address questions about the data?USDA NASS, Spatial Analysis Research SectionAttn: USDA NASS, Spatial Analysis Research Section staff1400 Independence Avenue, SW, Room 5029 South BuildingWashington, District of Columbia 20250-2001
USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS.RDD.GIB@usda.govWhy was the data set created?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.How was the data set created?From what previous works were the data drawn?SENTINEL-2 (source 1 of 14)European Space Agency (ESA), 2023, SENTINEL-2: Copernicus - European Commission, European Commission, Brussels (Belgium).Other_Citation_Details:The CDL used Sentinel-2 satellite imagery as one of the inputs from 2017-2023. 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 . The imagery was resampled to 30 meters to match Landsat spatial resolution. The resample used cubic convolution, rigorous transformation. Refer to for specific scene date, path, row and quadrants used as classification inputs for each state and year.Type_of_Source_Media: online download
Source_Scale_Denominator: 10 meter
Source_Contribution: Raw data used in land cover spectral signature analysis
Landsat (source 2 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS), 2023, Landsat TM/ETM/OLI/TIRS: USGS, EROS, Sioux Falls, South Dakota 57198-001.Other_Citation_Details:The CDL has used Landsat satellite imagery as a primary input throughout the entire history of the program from 1997 to current. The Landsat data are free for download through the following website . Additional information about Landsat data can be obtained at . Refer to for specific sensor, scene date, path and rows used as classification inputs for each state and year.Type_of_Source_Media: online download
Source_Scale_Denominator: 30 meter
Source_Contribution: Raw data used in land cover spectral signature analysis
NED (source 3 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2009, The National Elevation Dataset (NED): USGS, EROS Data Center, Sioux Falls, South Dakota 57198 USA.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 . Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.Type_of_Source_Media: online
Source_Scale_Denominator: 30 meter
Source_Contribution:spatial and attribute information used in land cover spectral signature analysisNLCD (source 4 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2021, National Land Cover Database 2019 (NLCD 2019): USGS, EROS Data Center, Sioux Falls, South Dakota 57198 USA.Other_Citation_Details:The most current publicly available NLCD is used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD Imperviousness and Tree Canopy Layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD can be found at . Refer to for the complete list of ancillary data sources used as classification inputs for each state and year.Type_of_Source_Media: online
Source_Scale_Denominator: 30 meter
Source_Contribution: Raw data used in land cover spectral signature analysis
FSA CLU (source 5 of 14)United States Department of Agriculture (USDA) Farm Service Agency (FSA), 2023, USDA, FSA Common Land Unit (CLU): USDA, FSA Aerial Photography Field Office, Salt Lake City, Utah 84119-2020 USA.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 .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverNCCPI (source 6 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2012, National Commodity Crop Productivity Index (NCCPI) Version 2.0: United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center, Lincoln, Nebraska USA.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: .Type_of_Source_Media: online
Source_Scale_Denominator: 30 meter
Source_Contribution: Ancillary input used in land cover spectral signature analysis
LandIQ (source 7 of 14)California Department of Water Resources (DWR), 2023, Statewide Land Use 2021 (Provisional): California Department of Water Resources (DWR), Sacramento, California 94236-0001 USA.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 and .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverLCRAS GIS Data (source 8 of 14)United States Department of Interior, Bureau of Reclamation, Lower Colorado Region, 2023, Lower Colorado River Water Accounting System (LCRAS) GIS data layer: United States Department of Interior, Bureau of Reclamation, Lower Colorado Region, Boulder City, NV 89006-1470, USA.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 .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverNASS Citrus Grove Data Layer (source 9 of 14)United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), 2023, USDA NASS Citrus Grove Data Layer: USDA NASS Florida Field Office, Maitland, Florida 32751-7057 USA.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.Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverFSAID (source 10 of 14)Florida Department of Agriculture and Consumer Services, 2023, Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase: Florida Department of Agriculture and Consumer Services, Tallahassee, Florida 32399-0800 USA.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 .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverLake Erie Vineyards GIS data (source 11 of 14)Cornell Cooperative Extension, Lake Erie Regional Grape Program, 2023, GIS Mapping of Lake Erie Vineyards: Lake Erie Regional Grape Program at CLEREL - Cornell University, Portland, NY, 14769 USA.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 .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land covernone (source 12 of 14)Gordon B. Jones, PhD, and Rick Hilton of Oregon State University; Karim Naguib of the Jackson County GIS Office, unknown, Pear and Vineyard Data for Jackson County, Oregon: unpublished, Central Point, Oregon 97502 USA.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.Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverUtah DWR Agriculture Check Polygons (source 13 of 14)Utah Division of Water Resources, 2023, Agriculture Check Polygons: Utah Division of Water Resources, Salt Lake City, Utah 84116 USA.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.Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverWSDA Crop Geodatabase (source 14 of 14)Washington State Department of Agriculture (WSDA), 2023, WSDA Crop Geodatabase: Washington State Department of Agriculture, Olympia, WA 98504-2560 USA.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 .Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverHow were the data generated, processed, and modified?Date: 2023 (process 1 of 1)OVERVIEW: 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 . 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 metadata webpages at 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 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 . The data is available free for download through CroplandCROS and the Geospatial Data Gateway . Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.Person who carried out this activity:
USDA NASS, Spatial Analysis Research SectionAttn: USDA NASS, Spatial Analysis Research Section staff1400 Independence Avenue, SW, Room 5029 South BuildingWashington, District of Columbia 20250-2001
USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS.RDD.GIB@usda.govWhat similar or related data should the user be aware of?How reliable are the data; what problems remain in the data set?How well have the observations been checked?
Below are the Overall Accuracy metrics for the crop-specific categories for the Continental United States 2016 to 2023 CDLs. Full accuracy reports for past years, states, and individual crop types are available at the official NASS metadata website noted above.2023 Cropland Data Layer81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.788 KAPPA2022 Cropland Data Layer80.9% OVERALL CROP ACCURACY, 19.1% ERROR, 0.780 KAPPA2021 Cropland Data Layer81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.787 KAPPA2020 Cropland Data Layer81.3% OVERALL CROP ACCURACY, 18.7% ERROR, 0.786 KAPPA2019 Cropland Data Layer81.5% OVERALL CROP ACCURACY, 18.5% ERROR, 0.789 KAPPA2018 Cropland Data Layer82.3% OVERALL CROP ACCURACY, 17.7% ERROR, 0.796 KAPPA2017 Cropland Data Layer82.2% OVERALL CROP ACCURACY, 17.8% ERROR, 0.800 KAPPA2016 Cropland Data Layer79.6% OVERALL CROP ACCURACY, 20.4% ERROR, 0.767 KAPPAThe 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 .How accurate are the geographic locations?
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 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 using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please reference the metadata on the Copernicus website for positional accuracy details.Where are the gaps in the data? What is missing?
Continental US coverage (2008 to current), partial US coverage (1997-2007)How consistent are the relationships among the observations, including topology?
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 . More information about the NLCD can be found at .How can someone get a copy of the data set?Are there legal restrictions on access or use of the data?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.Who distributes the data set?
USDA NASS Customer ServiceAttn: USDA NASS Customer Service Staff1400 Independence Avenue, SW, Room 5038-SWashington, District of Columbia 20250-2001
USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS.RDD.GIB@usda.govContact_Instructions:Please visit the official website for distribution details. The Cropland Data Layer is available free for download at CroplandCROS and the Geospatial Data Gateway . Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.What's the catalog number I need to order this data set?2023 Cropland Data LayerWhat legal disclaimers am I supposed to read?Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS maintains a Frequently Asked Questions (FAQ's) section at .How can I download or order the data?Availability in digital form:Data format:GEOTIFF in format GEOTIFFNetwork links:
Cost and order instructions:The CDL is available online and free for download at CroplandCROS , the Geospatial Data Gateway , and the NASS CDL website . Distribution questions can be directed to the NASS Customer Service Hotline at 1-800-727-9540.What hardware or software do I need in order to use the data set?If the user does not have software capable of viewing GEOTIF (.tif) file formats then we suggest using CroplandCROS .Who wrote the metadata?Metadata in various formats availabe at Data.Gov
Metadata Standard: ISO 19115-3Metadata Standard: ISO 19139Dates:Last modified: 31-Jan-2024
Metadata author:USDA NASS, Spatial Analysis Research SectionAttn: USDA NASS, Spatial Analysis Research Section Staff1400 Independence Avenue, SW, Room 5029 South BuildingWashington, District of Columbia 20250-2001
USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS.RDD.GIB@usda.govMetadata standard: FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)The layout and majority of content presented above was generated by mp version 2.9.50 on Thu Feb 01 13:27:09 2024
CDL PROGRAM HISTORYHow has the CDL Program changed over time? Originally, field preparation and digitizing work were performed in NASS Field Offices and the remote sensing analysis performed by the Spatial Analysis Research Section (SARS) of NASS. However, in 1997 SARS entered into a data sharing partnership with USDA's Foreign Agricultural Service and USDA's Farm Service Agency. The agreement provided access to Landsat 5 coverage in the states selected for the project by SARS. The first states covered with the data sharing partnership were Arkansas, North Dakota and South Dakota. Improvements in hardware along with software enhancements made program expansion possible for the 1999 growing season. NASS Research Development Division solicited additional states to find outside cooperators/partners to provide an analyst and hardware to perform duties associated with the Acreage Estimation Program. The Illinois and Mississippi State Field Offices were able to obtain partnership agreements with external State/Federal Agencies.
For crop year 2000, the states of Iowa and Indiana were added tothe Program. North Dakota was able to obtain a partner for the 2000crop year cooperatively with North Dakota State University (NDSU)through an EPA water quality grant for 5 years. Indiana was addedto the program for crop year 2000 also, but as a regional typecenter where the ground data collection and digitization wasperformed at the Indiana State Office, and the acreage estimationwas performed at the Illinois State Office.
For crop year 2001, the Missouri southeastern boot heel area wasadded to the program. All boot heel digitizing was performed by theMissouri Ag Statistics Service, and image processing duties were performed by the Arkansas Ag Statistical Service. Nebraska andWisconsin were added as pilot states, where all digitizing wasperformed by the Nebraska and Wisconsin Ag Statistics Service offices respectively, and image processing functions were performed by SARS. Maryland/Delaware were also added as a pilot program wheredigitizing was done by the University of Maryland Mid-AtlanticRESAC group, and image processing was performed by the SARSgroup.
For crop year 2002, Nebraska expanded to full state coverage, andWisconsin expanded to full state coverage in 2003. In 2002, a ten state Mid-Atlantic based Cropland Data Layer product was sponsored in part by a NASA/Raytheon/Synergy Project through Towson University, with thedigitizing and image analysis performed under contract by NASS. TheMid-Atlantic CDL products were based on the 2002 June AgriculturalSurvey and the Agriculture Coverage Evaluation Survey (ACES) thatcoincided with the 2002 Agricultural Census.
For crop year 2004, the IRS Resourcesat-1 AWiFS sensor was usedover Nebraska, Indiana and Arkansas to perform acreage analysis. The AR, IN and NE CDL's were released with both Landsat TMclassifications as well as AWiFS classifications. The AWiFS sensor has 56 meter spatial resolution, and five day repeat coverage. Thebest possible scene dates taken during the month of August 2004were used to create the AWiFS CDL products. A cooperativepartnership between University of Maryland Department of Geography and SARS helped process the Louisiana 2004 CDL. A Florida CDL for 2004 was released in February of 2007 using Landsat 5/7 imagery. The Florida CDL was the first CDL createdexclusively with See5, and it was the first usage of thesegmentation based gap filled Landsat-7 SLC-off imagery. Itincluded the first usage of the Farm Service Agency/Common LandUnit and the Florida Citrus Grove layer for ground truthtraining.
For crop year 2005, the Idaho Cropland Data Layer was created witha cooperative partnership between Utah State University, the UnitedPotato Growers of Idaho and NASS. This partnership produced both aLandsat TM and Resourcesat-1 AWiFS classification over the IdahoSnake River Plain. The 2005 Midwestern CDL update contained newAWiFS classifications and a revised Wisconsin TM basedclassification. The new AWiFS classifications cover Nebraska andNorth Dakota. The Wisconsin revision was performed under contractfor the Wisconsin State, Bureau of Environmental and OccupationalHealth and Department of Health and Family Services. The 2005Mississippi Delta Region was classified using the regression treeclassifier See5.0 available from www.rulequest.com over the 2001NLCD defined mapping Zone 45 for the States ofArkansas, Louisiana and Missouri. The Zone 45 classificationresults from See5.0 were overlaid on top of the Arkansas, Louisianaand Missouri bootheel, resulting in an accurate ag classificationand an enhanced non-ag land cover classification leveraging resultsfrom the 2001 NLCD products. The traditional pixel based PEDITORclassification covers the remaining parts of these states.
The 2006 Delta/Midwestern/Pacific Northwest CDL products coveredeleven states: AR, IL, IN, IA, LA, MO, MS, NE, ND, WA, WI. Illinoisand Indiana were processed with Peditor. The remaining States wereprocessed using See5 decision tree software. The Mississippi DeltaCDL and the remaining Midwestern and Prairie States were processedexclusively with See5 using the FSA Common Land Unit for groundtruth. The 2006 Washington CDL does have a smoothing algorithm applied to remove pixel scatter. This is the only state and year of CDL that has any level of post-classification smoothing.
The 2007 CDL product became operational in NASS delivering for thefirst time in-season acreage estimates for the October 2007 CropReport across all speculative corn and soybean states. Twenty-onestates total (AR, CA, IL, IN, IA, ID, KS, LA, MI, MN, MO, MS, MT,ND, NE, OH, OK, OR, SD, WA, WI) were processed into CDL's.Additionally, new CDL's were created for crop year 2006 for KS, MN,MO, OH, OK, SD. Michigan State University/Land Policy Instituteentered into a cooperative partnership with SARS and obtainedfunding to provide an image analyst to process Michigan.
The 2008 crop year is the first year that the entire ContinentalUnited States is covered by the CDL. Real-time CDL acreageestimates were produced for the June Ag Survey for winter wheat,the August Crop Report and the October Crop Report for corn andsoybeans. The 2008 CDL was reprocessed and released on 12/11/2017. The 2008 and 2009 Cropland Data Layers (CDL) for the entire Continental United States have been reprocessed and re-released at a 30 meter spatial resolution. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications.
The 2009 crop year again covered the entirecontinental United States and produced real-time CDL acreageestimates for the June Ag Survey and September Small Grain Summaryfor winter wheat, the August, September and October Crop Reportsfor corn, soybeans, rice and cotton. The original 2009 product wasreleased at 56 meters resolution but was re-released as a 30 meter product on 12/11/2017.
The 2008 and 2009 CDLs were reprocessed and re-released on 12/11/2017 for the entire Continental United States at a 30 meter spatial resolution. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications.
The 2010 CDL product was released the first week of January 2011co-incident with the release of the new CropScape web service.The 2010 product utilized Landsat TM/ETM+ and AWiFS imagery for productionof a 30m product covering the Continental United States.
The 2011 CDL product was released January 31, 2012. The 2011product utilized Deimos-1, UK-DMC 2, Landsat TM/ETM+, and AWiFSimagery for production of a 30m product covering the Continental United States. Coincident withthe release of the 2011 product, the entire historical CDL catalogwas re-released with minor category code and class name revisions.These revisions were done to eliminate redundant or unusedcategories. Please view the 2011 crosswalk document for a detailed listing of the revisions.
The 2012 CDL product was released January 31, 2013. The 2012product utilized Deimos-1, UK-DMC 2, and Landsat TM/ETM+ imageryfor production of a 30m product covering the Continental United States.
The 2013 CDL product was released January 31, 2014. The 2013product utilized Deimos-1, UK-DMC 2, and Landsat 8 imageryfor production of a 30m product covering the Continental United States. Coincident withthe release of the 2013 product, the entire historical CDL catalogwas re-released with minor category code and class name revisions.These revisions were done to eliminate redundant or unusedcategories. Please view the 2013 crosswalk document for a detailed listing of the revisions.
The 2014 CDL product was released February 2, 2015. The 2014 CDL product utilized Deimos-1, UK-DMC 2, and Landsat 8 imageryfor production of a 30m product covering the Continental United States.
The 2015 CDL product was released February 12, 2016. The 2015 CDL product utilized Deimos-1, UK-DMC 2, and Landsat 8 imageryfor production of a 30m product covering the Continental United States.
The 2016 CDL product was released February 3, 2017. The 2016 CDL product utilized Deimos-1, UK-DMC 2, and Landsat 8 imageryfor production of a 30m product covering the Continental United States. Beginning with the 2016 CDL season we are creating CDL accuracy assessments using unbuffered validation data. These "unbuffered" accuracy metrics will now reflect the accuracy of field edges which have not been represented previously. This admission of modestly inflated accuracy measures does not render past assessments useless. By providing both buffered and unbuffered validation scenarios for 2016 gives guidance on the bias. There are no plans to create unbuffered accuracy assessments for prior CDL seasons.
The 2017 CDL product was released January 26, 2018. The 2017 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States.
The 2018 CDL product was released February 15, 2019. The 2018 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. A new CDL category was added in 2018, Code 215 - Avocados.
The 2019 CDL product was released February 5, 2020. The 2019 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. The 2007-2018 CDLs used FSA CLU data for agricultural training with a 30 meter inward buffer applied. The inward buffering removes spectrally mixed field edge pixels from the land cover classifier. Starting with the 2019 CDL products, the inward buffer has been reduced from 30 meters to 15 meters. The result is a noticeable increase in crop identification at field borders which impacts the CDL, the Cultivated Layer, and Crop Frequency Layers. The newly published USGS NLCD 2016 was used as training for the non-agricultural component of the 2019 CDL. A new CDL category was added in 2019, Code 228 - Double-Cropped Triticale/Corn.
The 2020 CDL product was released February 1, 2021. The 2020 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. There were no new CDL categories added in 2020.
The 2021 CDL product was released February 14, 2022. The 2021 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. There were no new CDL categories added in 2021.
The 2022 CDL product was released January 30, 2023. The 2022 CDL product utilized satellite imagery from the Landsat 8 and Landsat 9 OLI/TIRS sensors, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2A and -2B sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. There were no new CDL categories added or changes in processing methodology in 2022.
The 2023 CDL product was released January 31, 2024. The 2023 CDL product utilized satellite imagery from the Landsat 8 and Landsat 9 OLI/TIRS sensors and the ESA SENTINEL-2A and -2B sensors collected during the current growing season. The spatial resolution is 30 meters covering the Continental United States. There were no new CDL categories added or changes in processing methodology in 2023.How has the methodology used to create the CDL changed over the program's history?
The classification process used to create older CDLs (prior to 2006) was based on a maximum likelihood classifier approach using in-house software. The pre-2006 CDL's relied primarily on satellite imagery from the Landsat TM/ETM satellites which had a 16-day revisit. The in-house software limited the use of only two scenes per classification area. The only source of ground truth was the NASS June Area Survey (JAS). The JAS data is collected by field enumerators so it is quite accurate but is limited in coverage due to the cost and time constraints of such a massive annual field survey. It was also very labor intensive to digitize and label all of the collected JAS field data for use in the classification process. Non-agricultural land cover was based on image analyst interpretations.
Starting in 2006, NASS began utilizing a new satellite sensor, new commercial off-the-shelf software, more extensive training/validation data. The in-house software was phased out in favor of a commercial software suite, which includes Erdas Imagine, ESRI ArcGIS, and Rulequest See5. This improved processing efficiency and, more importantly, allowed for unlimited satellite imagery and ancillary dataset inputs. The new source of agricultural training and validation data became the USDA Farm Service Agency (FSA) Common Land Unit (CLU) Program data which was much more extensive in coverage than the JAS and was in a GIS-ready format. NASS also began using the most current USGS National Land Cover Dataset (NLCD) dataset to train over the non-agricultural domain. The new classification method uses a decision tree classifier.
NASS continues to strive for CDL processing improvements, including our handling of the FSA CLU pre-processing and the searching out and inclusion of additional agricultural training and validation data from other State, Federal, and private industry sources. New satellite sensors are incorporated as they become available. Currently, the CDL Program uses the Landsat 8 and 9 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 A and B sensors. Imagery is downloaded daily throughout the growing season with the objective of obtaining at least one cloud-free usable image every two weeks throughout the growing season.Common User QuestionsIs the Cropland Data Layer available in a shapefile format? We do not offer the data in a vector format, such as shapefile. The CDL data can be downloaded in a raster-based GeoTIFF file format and used in most common GIS software. In ESRI ArcGIS you would most likely require the 'Spatial Analyst' extension to perform any in-depth GIS applications using the GeoTIFF file. And any common image processing software, such Erdas Imagine, ENVI or PCI, should be able to perform basic image processing/GIS applications using the GeoTIFF file. This type of pixel-based data does not lend itself to being converted to vector since the resulting polygon file would be enormous. Depending on the size of area you are studying it is technically possible to convert CDL data to a shapefile, but it would have to be a rather small area such as a single county or smaller.
If you do convert the data to a shapefile format and want to add the CDL category names in ESRI ArcGIS, then start by downloading this spreadsheet file cdl_codes_names.xlsx that lists all CDL codes and category names. Open this file and change the "Code" column header to match the name of the attribute field in your newly created shapefile. Then open both the excel file and the shapefile in ArcGIS. Right click on the shapefile in the ArcGIS Table of Contents and do a JOIN on the commonly named "code" attribute field. You can then right click on the shapefile and use Data > Export Data to save a new shapefile with the category names added.What differences can be expected when comparing CDL-based acreage and official NASS statistics?
Users should be aware of the potential limitations of acreage summaries that are based on only pixel counting. Most land cover classification datasets will contain some level of counting bias (typically downward). Pixel counting is usually downward biased when compared to the official estimates. Counting pixels and multiplying by the area of each pixel will result in biased area estimates and should be considered raw numbers needing bias correction. Official crop acreage estimates at the state and county level are available at QuickStats.
Here are a list of references discussing the subject matter of pixel counting and estimation:
a) Gallego F.J., 2004, Remote sensing and land cover area estimation. International Journal of Remote Sensing. Vol. 25, n. 15, pp. 3019-3047.
b) European Commission, Joint Research Centre, MARS; Best practices for crop area estimation with Remote Sensing - Section 4.1.1.
c) Czaplewski, R. L. (1992). Misclassification bias in areal estimates. Photogrammetric Engineering and Remote Sensing, 58, 189-192.
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