[Keyword='Geographic Information Systems']
USDA, National Agricultural Statistics Service, Cropland Data Layer for the United StatesEntry ID: USDA_NASS_CROPLAND
Abstract: The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer with a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. Some Cropland Data Layer states used Landsat 5 TM and/or Landsat 7 ETM+ ... satellite imagery to supplement the classification. Ancillary classification inputs include: the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2001 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
Purpose: The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
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Data Set Citation
Dataset Originator/Creator: United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)
Dataset Title: USDA, National Agricultural Statistics Service, Cropland Data Layer for the United States
Dataset Release Date: 2011-01-10
Dataset Release Place: USDA, NASS Marketing and Information Services Office, Washington, D.C.
Dataset Publisher: USDA, NASS
Version: 2011Online Resource: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
Start Date: 2010-01-01Stop Date: 2010-12-31
Latitude Resolution: 30m
Longitude Resolution: 30m
Horizontal Resolution Range: 30 meters - < 100 meters
Quality 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 Dataset (NLCD 2001). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
The official DVD contains additional accuracy ... assessment information that is not available through the Geospatial Data Gateway in the form of an associated confidence layer. The following description of the confidence layer is taken from the document entitled 'MDA_NLCD_User_Guide.doc' which is available free for download with the NLCD Mapping Tool at http://www.mrlc.gov/. The Confidence Layer "spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. This is useful in that the user can see the spatial representation of distribution and magnitude of error or confidence for a given classification... This error layer represents a percent confidence associated with each rule and output categorical, classified value. It is expressed as a percentage of confidence. A value of zero would therefore have a low confidence (always wrong), while a value of 100 would have a very high confidence (always right)." For more information on the use of confidence layers please refer to the following paper: Liu, Weiguo, Sucharita Gopal and Curtis E. Woodcock, 2004. Uncertainty and confidence in land cover classification using a hybrid classifier approach, Photogrammetric Engineering & Remote Sensing, 70(8):963-971. Ultimately, however, the confidence value is not a measure of accuracy for a given pixel but rather a how well it fit within the decision tree ruleset.
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.
Access Constraints None
Use Constraints The USDA, NASS Cropland Data Layer 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. If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the freeware browser ESRI ArcGIS Explorer http://www.esri.com/software/arcgis/explorer/.
Data Set Progress
Role: TECHNICAL CONTACT
Email: HQ_RDD_GIB at nass.usda.gov
3251 Old Lee Highway, Room 305
Province or State: VA
Postal Code: 22030-1504
Role: DIF AUTHOR
Phone: (301) 614-6898
Email: Tyler.B.Stevens at nasa.gov
NASA Goddard Space Flight Center Global Change Master Directory
Province or State: MD
Postal Code: 20771
Creation and Review Dates
DIF Creation Date: 2010-06-07
Last DIF Revision Date: 2011-04-22