CropScape | FAQ's | Metadata | National Download | Other CDL Citations
The geospatial data product called the Cropland Data Layer (CDL) is hosted on CropScape (https://nassgeodata.gmu.edu/CropScape/). The CDL is a raster, geo-referenced, crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. All historical CDL products are available for use and free for download through CropScape. For more information about the CDL Program please refer to the metadata for the particular state and year you are interested at the following web page: (/Research_and_Science/Cropland/metadata/meta.php).
There are four buttons in the upper right-hand corner of the CropScape website that offer tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".
The following list summarizes the new functional capabilities and enhancements that were made to CropScape for the January 31, 2013 release:
a. More map layers including river, lake and road layers at national and regional level were added;
b. A Cultivated Layer generated from the 2007-2011 CDL data was added;
c. A Swipe function was added. This new feature allows users to swipe the CDL images of two years in the map panel to view the differences interactively;
d. AOI import and export function was added. It allows user to import and export the area of interest (AOI) in the format of GML and ESRI Shapefile;
e. Added the capability to upload a user-defined data layer in a raster GeoTIFF file format to the portal and add it as an auxiliary layer using WMS;
f. Added the option of "Displaying Crop Categories Only" in the AOI Statistics and Change Analysis results;
g. Added the capability to add a user-defined Change Analysis as a new data layer in the map panel;
h. Created a developer guide and added a toolbar shortcut (https://nassgeodata.gmu.edu/CropScape/devhelp/help.html) for users to utilize Web geospatial data services (OGC WMS and WCS) and Web geoprocessing services;
i. Reprocessed all updated CDL data from NASS and regenerated the related configuration files;
j. Updated all category codes and names in the source code of server side and client side;
k. Updated help files and demonstration video with the new functions;
l. Compressed the mosaicked CDL files by year and offered their links for download and use;
m. Added the operation of GetCDLPDF in the existing CDL Web service;
n. Added a filter to ignore minor acreage changes when doing a Change Analysis (ignores less than 10 pixels when comparing CDL files with over than 100,000 pixels);
o. Added a CropScape list of publications link (https://nassgeodata.gmu.edu/CropScape/publication.htm);
p. Generated the Crop Mask/Cultivated Layer and configured WMS for this data layer for use in CropScape and the VegScape Crop Condition Web portal.
Further CropScape enhancements will depend on user feedback and resources available.
The Cropland Data Layer (CDL) was created by the USDA, National Agricultural Statistics Service, Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. The most current data is available free for download along with extensive metadata, FAQs, and other detailed technical information at the following website: /Research_and_Science/Cropland/SARS1a.php. NASS developed both the CropScape and VegScape web services in cooperation with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA.
The purpose of the Cropland Data Layer Program is to use satellite imagery to provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and to produce digital, crop-specific, categorized geo-referenced output products.
There will be differences between CropScape and official NASS estimates when comparing acreage statistics at the state, district, and county levels. Statistics generated by CropScape are dependent upon pixel counting. 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 http://www.nass.usda.gov/.
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.
Zoom to the national scale map, choose the year that you want to download and click on the "Download Defined Area of Interest Data" button on the toolbar. Respond "yes" to the download confirmation question. The downloadable file will be a Winzip compressed file containing the CDL in a GeoTIFF (TIF) file format.
The following downloadable jpeg files are color legends by year for the Continental United States CDLs:
US_2015_CDL_legend.jpg
US_2014_CDL_legend.jpg
US_2013_CDL_legend.jpg
US_2012_CDL_legend.jpg
US_2011_CDL_legend.jpg
US_2010_CDL_legend.jpg
US_2009_CDL_legend.jpg
US_2008_CDL_legend.jpg
The following downloadable zip files contain color legends for each individual CDL state organized by year:
CDL_Legends_2015.zip
CDL_Legends_2014.zip
CDL_Legends_2013.zip
CDL_Legends_2012.zip
CDL_Legends_2011.zip
CDL_Legends_2010.zip
CDL_Legends_2009.zip
CDL_Legends_2008.zip
CDL_Legends_2007.zip
CDL_Legends_2006.zip
CDL_Legends_2005.zip
CDL_Legends_2004.zip
CDL_Legends_2003.zip
CDL_Legends_2002.zip
CDL_Legends_2001.zip
CDL_Legends_2000.zip
CDL_Legends_1997-1999.zip
To view the Cropland Data Layer data on CropScape with just one or two commodities shown at the national, state, district and/or county levels: 1) Select "Define Area of Interest by State/ASD/County" or "Define Area of Interest" or "Import Area of Interest" from the top toolbar 2) Once you have an area of interest (AOI) defined select the "Area of Interest Statistics" from the top toolbar 3) Choose the commodity of interest from the popup, you can choose one or many 4) Export the selected crop(s) for mapping to create a graphic containing only the selected crop(s) in your defined AOI.
The 2015 attribute data is available at CropScape_2015_Stats.xlsx.
The 2014 attribute data is available at CropScape_2014_Stats.xlsx.
The 2013 attribute data is available at CropScape_2013_Stats.xlsx.
The 2012 attribute data is available at CropScape_2012_Stats.xlsx.
The 2011 attribute data is available at CropScape_2011_Stats.xlsx.
The 2010 attribute data is available at CropScape_2010_Stats.xlsx.
The 2009 attribute data is available at CropScape_2009_Stats.xlsx.
The 2008 attribute data is available at CropScape_2008_Stats.xlsx.
The 2015 accuracy data is available at CDL_2015_accuracy_assessments.zip.
The 2014 accuracy data is available at CDL_2014_accuracy_assessments.zip.
The 2013 accuracy data is available at CDL_2013_accuracy_assessments.zip.
The 2012 accuracy data is available at CDL_2012_accuracy_assessments.zip.
The 2011 accuracy data is available at CDL_2011_accuracy_assessments.zip.
The 2010 accuracy data is available at CDL_2010_accuracy_assessments.zip.
The 2009 accuracy data is available at CDL_2009_accuracy_assessments.zip.
The 2008 accuracy data is available at CDL_2008_accuracy_assessments.zip.
The CropScape website offers a "Cultivated Layer" or "Crop Mask Layer". It is based on Cropland Data Layers from the most recent five years of CDL data and is updated annually. An Erdas Imagine Spatial Model is used to create the Cultivated Layer. The processing logic is as follows. If a pixel is identified as cultivated in at least two out of the five years of CDL data then it is assigned to the 'Cultivated' category. The exception is that all pixels identified as cultivated in the most recent year are assigned to the 'Cultivated' category regardless of whether or not they were cultivated in the previous four years of CDL data.
The Crop Frequency Layers identify crop specific planting frequency and are based on land cover information derived from every year of available CDL data beginning with the 2008 CDL, the first year of full Continental U.S. coverage. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.
The Cultivated Layer and Crop Frequency Data Layers with accompanying metadata detailing the methodology are available for download at /Research_and_Science/Cropland/Release/.
Extensive metadata files are available by state and year in HTM and XML file formats at the following website: (/Research_and_Science/Cropland/metadata/meta.php).
USDA National Agricultural Statistics Service Cropland Data Layer. {YEAR}. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed {DATE}; verified {DATE}). USDA-NASS, Washington, DC.
Distribution issues can be directed to the NASS Customer Service Hotline at 1-800-727-9540. Content and technical questions can be directed to the NASS Spatial Analysis Research Section (SARS) at 703-877-8000 or email HQ_RDD_GIB@nass.usda.gov.
To create a pixel "count" field in the attribute table of the downloaded CDL use the "Build Raster Attribute Table" Function in ESRI ArcGIS. In ESRI ArcGIS Version 9.3 and 10 this function is located at ArcToolbox > Data Management Tools > Raster > Raster Properties > Build Raster Attribute Table. Specify the downloaded CDL tif file as the Input Raster and accept all other defaults and click OK. After it has run successfully, a new "Count" data field is added to the attribute table. Count represents a raw pixel count. To calculate acreage multiply the count by the square meters conversion factor which is dependent upon the CDL pixel size. The conversion factor for 30 meter pixels is 0.222394. The conversion factor for 56 meter pixels is 0.774922.
If your downloaded CDL .tif file does not contain category names, then you can add them using the following instructions. Download the file: generic_cdl_attributes.tif.vat.dbf. This generic file contains all possible CDL colors and category names. As long as the .tif file and the .tif.vat.dbf file have the same file name, then the category names will load automatically in ArcMap. So, change the file name (not extension) of the generic_cdl_attributes.tif.vat.dbf to match the file name of the downloaded CDL .tif file. Then add the .tif file as a layer in ArcMap. The category names will display in the Table of Contents window.
Example 1 - If the downloaded .tif file is: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif Change the generic_cdl_attributes.tif.vat.dbf file name to: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif.vat.dbf
Example 2 – If you renamed the downloaded .tif file to MyCDL.tif, then rename the generic_cdl_attributes.tif.vat.dbf file name to MyCDL.tif.vat.dbf.
To generate statistics in Erdas Imagine, go to Tools > Image Information then click on "Compute the statistics." If you have the TIF file open in a Viewer, then you will have to close it and reopen the TIF file. Now when you view the attribute information, there should be a Histogram column, which represents the pixel count per category. To calculate acreage multiply the count by the square meters conversion factor which is dependent upon the CDL pixel size. The conversion factor for 30 meter pixels is 0.222394. The conversion factor for 56 meter pixels is 0.774922.
You first must build statistics for the TIF file as outlined in the question above. To add category names, open the TIF in a Viewer and select Raster > Attributes. In the Raster Attribute Editor select Edit > Add Class Names. This new "Class Names" column can be populated manually or you can download this prepared DAT file located at: /Research_and_Science/Cropland/docs/cdl_class_names.zip. Unzip the CDL names and colors file and save the DAT files to your computer. Then in the Raster Attribute Editor highlight the "Class Names" column by left-clicking on the header of the Class Names column. Next, right-click on the Class Names column and select the Import option. Specify the CDL_Names DAT file as the file to import and this will add all possible CDL class names to your TIF attribute table. You can add colors by importing the CDL_Colors DAT file similar to the steps used to add the class names.
The pixel counting algorithm used by CropScape is straightforward. An area of interest (AOI) is defined by an enclosed boundary. The AOI is then rasterized and the pixels that fall within this AOI are counted. ArcGIS server is not used in this application. Please refer to Question 61 or 64 on this FAQs webpage for details on how acreage calculated on CropScape will compare to official NASS acreage estimates.
CropScape allows users to analyze and interact with areas less than 2,000,000 square kilometers. However, users can download the entire national CDL by year by following the instructions in this FAQ question in this FAQ question which could then be used to perform analysis using their own GIS or image processing software.
If you are using Microsoft Internet Explorer, try changing the default timeout value (greater than 100000ms if your network connection is slow). Here is a link detailing how to change the default timeout value: http://support.microsoft.com/kb/813827. You could also try using a web browser other than Internet Explorer, such as Firefox or Google Chrome.
This issue is caused by security controls of Internet Explorer when rendering a state with a large boundary file, which can take a long time depending on your internet connection speed. There are three possible solutions:
Solution 1: If you get a window that says "...Do you want to abort the script?", click the "NO" button to continue.
Solution 2: Follow the technical support at http://support.microsoft.com/kb/175500 to download a patch and fix this problem automatically.
Solution 3: Try using another browser, such as Firefox, Safari, or Google Chrome.
The George Mason University group responsible for creating the CropScape website offer an online developer's guide available at https://nassgeodata.gmu.edu/CropScape/devhelp/help.html.
The George Mason University group responsible for creating CropScape developed standard Web service for invocations or workflow in other web geospatial applications available at http://nassgeodata.gmu.edu:8080/axis2/services/CDLService?wsdl. There are 7 operations, including GetCDLStat, GetCDLImage, GetCDLComp, GetCDLFile, GetCDLValue, ExtractCDLByValues, and GetCDLPDF. This Web service supports HTTP GET/KVP POST/XML and SOAP encoding. Click here (standard Web service examples) for examples of the HTTP GET requests.
The WMS is implemented and is available to the general public. It is OGC standard compliant. The CDL can be served as a data layer from the user's application. If you receive an error when adding to ArcCatalog that reads "Could not add the specified data object to the map. Failed to open raster dataset" then try changing the WMS default version to 1.0.0 when you add the WCS.
Please use the following links: GetCapabilities and GetMap Example. Only EPSG:4326 the customized ESPG:102004 (USGS Albers) are supported in the GetMap Request now.
First, you can access the CDL WMS at: http://129.174.131.6/cgi-bin/wms_cdlall?. However, the default WMS GetCapabilities request in ArcGIS Explorer or ArcCatalog is version 1.3.0 with EXCEPTIONS=application/vnd.ogc.se_xml, which is not standard format (XML, INIMAGE or BLANK). Try changing the WMS version from default 1.3.0 to 1.1.0 when adding to the WMS server. For example screenshots, click here (AddWMS.jpg) and here (Preview.jpg).
ArcGIS Explorer Online only supports map, image and feature services from ArcGIS Server (http://help.arcgis.com/en/arcgisonline/help/index.html#/Creating_maps/010q0000001n000000/ADDING_LAYERS). For ArcGIS Explorer, CDL WMS could be added as one layer from GIS services. You can access the CDL WMS at http://129.174.131.6/cgi-bin/wms_cdlall?.
You can learn how to add a legend of a WMS layer in ArcGIS Explorer Desktop at http://webhelp.esri.com/arcgisexplorer/900/en/legend_window.htm.
Currently, we do not define the legend as a sublayer of CDL layers in our CDL WMS, so the legend for your selected CDL layers could not be displayed like the guide suggests. But, you still could try to access the legend by sending a request: http://129.174.131.6/cgi-bin/wms_cdlall?version=1.0.0&service=wms&request=getlegendgraphic&layer=cdl_2009&format=image/png
You can generate a kml file for the CDL data of the area of interest in CropScape, then add the kml file as one layer in ArcGIS Explorer Desktop, then right click data layer, and select "Show Popup", and the legend will be shown in the popup window.
CropScape is optimized for use with Adobe Flash player. Please upgrade your Adobe Flash player at http://www.adobe.com/.
You will need to write JavaScript code using the Bing Map API to add a kml layer, please follow the instructions at: http://msdn.microsoft.com/en-us/library/cc316942.aspx.
Distribution issues can be directed to the NASS Customer Service Hotline at 1-800-727-9540. Content and technical questions can be directed to the NASS Spatial Analysis Research Section (SARS) at 703-877-8000 or email HQ_RDD_GIB@nass.usda.gov.
Extensive metadata records are available by state and year at the following webpage: (/Research_and_Science/Cropland/metadata/meta.php).
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 to the Program. North Dakota was able to obtain a partner for the 2000 crop year cooperatively with North Dakota State University (NDSU) through an EPA water quality grant for 5 years. Indiana was added to the program for crop year 2000 also, but as a regional type center where the ground data collection and digitization was performed at the Indiana State Office, and the acreage estimation was performed at the Illinois State Office.The purpose of the Cropland Data Layer Program is to use satellite imagery to provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and to produce digital, crop-specific, categorized geo-referenced output products.
The entire archive of CDL products are available free at https://nassgeodata.gmu.edu/CropScape/ and are also free for download through the USDA NRCS Geospatial Data Gateway. The most current year of CDL data is available for download at the SARS website: /Research_and_Science/Cropland/SARS1a.php
Yes, the NASS Cropland Data Layer has no copyright restrictions. The CDL is considered public domain and free to redistribute. However, NASS would appreciate acknowledgment for the usage of our CDL product. The preferred citation is as follows:
USDA National Agricultural Statistics Service Cropland Data Layer. {YEAR}. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed {DATE}; verified {DATE}). USDA-NASS, Washington, DC.
The CropScape website is meant to eliminate the need for DVD production. All CDL data products are available through CropScape and the SARS website.
The CDL is processed using the Albers Equal-Area Conic Projection with a spheroid of GRS 1980 and datum of NAD83. The downloadable zip files from the SARS website and CropScape are offered in the native Albers projection.
In order to conform to Geospatial Data Gateway technical specifications, any CDL data downloaded through the Geospatial Data Gateway is re-projected from Albers to the dominant Universal Transverse Mercator (UTM) zone with a spheroid and datum of WGS84. The one exception to the UTM projection is for Wisconsin. Wisconsin is projected using the Wisconsin Transverse Mercator (WTM) projection. This WTM projection is based on the 1991 adjustment to NAD83, and is called WTM83/91. Projection parameters and additional information about WTM83/91 is posted on the DNR website: http://dnr.wi.gov/maps/gis/wtm8391.html
WTM83/91 Parameters
Projection: Transverse Mercator
Scale Factor at Central Meridian: 0.9996
Longitude of Central Meridian: 90 Degrees West (-90 Degrees)
Latitude of Origin: 0 Degrees
False Easting: 520,000
False Northing: -4,480,000
Unit: Meter
Horizontal Datum: NAD83, 1991 Adjustment (aka HPGN or HARN)
The CDL data is available in a raster-based GeoTIFF (.TIF) file format. The GeoTIFF for a single CDL data layer will have at least three associated files: .tif, .tfw, .aux, and possibly a .vat.dbf file. Please keep all associated files in the same directory as the GeoTIFF for proper viewing in ArcGIS or Erdas Imagine. Older CDLs may also be available in an ERDAS Imagine (.img) file format. The Erdas Imagine file will have at least two files associated with it: .img and .rrd and possibly .ige or .rde files.
The classification process used to create the CDL prior to 2006 was based on a maximum likelihood classifier approach using an in-house software package called Peditor. The pre-2006 CDL's relied solely 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 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, and began using the USGS National Land Cover Dataset (NLCD) to help identify non-agricultural land cover. 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. Combined with the rapid revisit of the new satellite sensor, this eliminated cloud contamination issues that plagued the CDL products prior to 2006. The new satellite was the AWiFS sensor on the Resourcesat-1 satellite. It has a resolution is 56 meters, or .77 acres, and revisits the same area approximately every 5 days. NASS also began supplementing the CDL classification process with MODIS data. The new source of agricultural training and validation data became the USDA Farm Service Agency (FSA) Common Land Unit (CLU) Program data. NASS also began using the most current NLCD dataset to train over the non-agricultural domain.
The new methodology uses a decision tree classifier as opposed to the maximum likelihood classifier. 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.
NASS continues to strive for CDL processing improvements. We continue to improve our FSA Form 578/CLU pre-processing methodology. The 2011 and 2012 CDLs used additional satellite imagery from the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors. The 2013 CDL utilized satellite imagery from the new Landsat 8 sensor. NASS continues to seek additional agricultural training and validation data from other State, Federal, and private industry sources. Please refer to Question 67 on this FAQs webpage to learn more about how the handling of grass and pasture related categories has evolved over the history of the CDL Program.
The CDL program became operational with one state in 1997, Arkansas. CDL coverage prior to 2008 is listed, with 2008 providing the first annual coverage for the Continental United States. Please visit the SARS website for a list of all states and years of available CDL data.
All CDL data for crop year 2007 and newer provide statewide cloud-free coverage. For CDL years 2006 and earlier, please reference the official metadata files to verify the extent of coverage and level of cloud contamination.
The CDL is released to the public in January following the end of the typical US growing season. Prior to the public release the CDL is considered confidential and market sensitive during the growing season and cannot be released until after the official NASS year end area county estimates are published. Furthermore, the CDL is considered preliminary during the growing season and could be misleading to our users, as we continue to receive updated ground truth and satellite imagery throughout the season.
When downloading the CDL using the NRCS Geospatial Data Gateway, all available years of CDL production for the requested state are included in a single compressed WinZIP file. Geospatial Data Gateway technical restrictions do not allow us to offer the CDL by individual state/year. The zip file will include all years of CDL data for the requested state in a GeoTIFF (.tif) file format projected in UTM and the accompanying metadata.
Below are instructions for downloading from the NRCS Geospatial Data Gateway (http://datagateway.nrcs.usda.gov/):
1. Go to the website: http://datagateway.nrcs.usda.gov/
2. Click on the "Get Data" button on the upper right hand side of the page
3. Select the state of interest from the drop down list
4. Select a single county. Your download will include the entire state regardless of what county you select
5. Click on "Submit Selected Counties"
6. Place a checkmark next to "Cropland Data Layer by State" under the "Land Use Land Cover" category. Click continue
7. Select "FTP" from the "Delivery" category. Click continue
8. Fill out all fields marked with a "*"
9. Click continue
10. Review order for accuracy then click "Place Order"
11. Note your order number. You will receive an email with a link to download your order.
If you already have GIS capability, you should be able to work with the downloadable GeoTIFF files directly in your software. If you do not have software capable of viewing a GeoTIFF (.tif) file format then we suggest using the freeware browser ESRI ArcReader. Some users have reported issues with viewing the CDL using ENVI software. NASS suggests using the GeoTIFF file format with ENVI and ensuring that the three associated files (.tif, .tfw, and .aux) are all kept together.
Prior to the 2006, the CDL processing was done entirely with in-house maintained software called PEDITOR. The PEDITOR classification was based on a maximum likelihood classifier. Limitations to the software also made it impossible to use more than two satellite scenes per classification, which was a significant hindrance when trying to minimize cloud coverage.
Beginning with the 2006 CDL products, the CDL program phased out the use of PEDITOR and transitioned to a commercial software suite. 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 MRLC NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine.
The CDL Program uses medium resolution satellite imagery. CDL products prior to 2006 relied primarily on Landsat 4/5/7. Beginning with the 2006 CDL products, the CDL program transitioned to using the sensor on the IRS-P6 Resourcesat-1 satellite. Also beginning in 2006, the MODIS, 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) from the NASA Terra satellite was used as an ancillary input to the CDL classification process. Additionally, the USGS policy of opening up the Landsat archive for free access in 2009 delivered the potential for the CDL to deliver in-season and national level agricultural monitoring capability. The 2011 CDL program added two additional sensors from the Disaster Monitoring Constellation (DMC) satellites, the Deimos-1 and the UK-DMC 2. Currently, it is too costly to use higher resolution satellites to perform crop acreage estimation over large areas. The 2013 to the most current CDL used Landsat 8 and DMC imagery.
Detailed accuracy assessment tables are published within the official metadata files. Generally, the large area row crops have producer accuracies ranging from mid 80% to mid 90%. The full error matrices used to create the accuracy assessment information contained within the metadata files is available for download in Question 11 and 54 of this FAQs webpage.
Prior to 2006, the Landsat TM/ETM categorized images were co-registered to MDA/EarthSat Inc's ortho-rectified GeoCover Stock Mosaic images using automated block correlation techniques. The resulting correlations were applied to each categorized image and then added to a master image or mosaic using NASS' in-house software, PEDITOR. The GeoCover Stock Mosaics are within 50 meters root mean squared error overall.
Newer Cropland Data Layers (2006 to current) retain the spatial attributes of the input imagery. The AWiFS imagery has a positional accuracy of 60 meters at the circular error at the 90 percent confidence level (CE90). CE90 is a standard metric often used for horizontal accuracy in map products and can be interpreted as 90% of well-defined points tested must fall within a certain radial AWiFS distance. The Landsat 4/5/8 imagery is obtained via download from the USGS Global Visualization Viewer (Glovis) website (http://glovis.usgs.gov/). Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 and DMC-UK 2 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.
The following downloadable jpeg files are color legends by year for the Continental United States CDLs:
US_2015_CDL_legend.jpg
US_2014_CDL_legend.jpg
US_2013_CDL_legend.jpg
US_2012_CDL_legend.jpg
US_2011_CDL_legend.jpg
US_2010_CDL_legend.jpg
US_2009_CDL_legend.jpg
US_2008_CDL_legend.jpg
The following downloadable zip files contain color legends for each individual CDL state organized by year:
CDL_Legends_2015.zip
CDL_Legends_2014.zip
CDL_Legends_2013.zip
CDL_Legends_2012.zip
CDL_Legends_2011.zip
CDL_Legends_2010.zip
CDL_Legends_2009.zip
CDL_Legends_2008.zip
CDL_Legends_2007.zip
CDL_Legends_2006.zip
CDL_Legends_2005.zip
CDL_Legends_2004.zip
CDL_Legends_2003.zip
CDL_Legends_2002.zip
CDL_Legends_2001.zip
CDL_Legends_2000.zip
CDL_Legends_1997-1999.zip
The 2015 attribute data is available at CropScape_2015_Stats.xlsx.
The 2014 attribute data is available at CropScape_2014_Stats.xlsx.
The 2013 attribute data is available at CropScape_2013_Stats.xlsx.
The 2012 attribute data is available at CropScape_2012_Stats.xlsx.
The 2011 attribute data is available at CropScape_2011_Stats.xlsx.
The 2010 attribute data is available at CropScape_2010_Stats.xlsx.
The 2009 attribute data is available at CropScape_2009_Stats.xlsx.
The 2008 attribute data is available at CropScape_2008_Stats.xlsx.
Below is a link to a WinZIP file containing CSV spreadsheets that summarize the pixel counts and acreage for all US counties for each land cover category for the CDL years 2007 through 2014. These are raw pixel counts and are not official NASS estimates.
The 2007-2014 CDL pixel counts and acreage county summaries are available at County_Pixel_Count.zip.
The 2015 accuracy data is available at CDL_2015_accuracy_assessments.zip.
The 2014 accuracy data is available at CDL_2014_accuracy_assessments.zip.
The 2013 accuracy data is available at CDL_2013_accuracy_assessments.zip.
The 2012 accuracy data is available at CDL_2012_accuracy_assessments.zip.
The 2011 accuracy data is available at CDL_2011_accuracy_assessments.zip.
The 2010 accuracy data is available at CDL_2010_accuracy_assessments.zip.
The 2009 accuracy data is available at CDL_2009_accuracy_assessments.zip.
The 2008 accuracy data is available at CDL_2008_accuracy_assessments.zip.
USDA National Agricultural Statistics Service Cropland Data Layer. {YEAR}. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed {DATE}; verified {DATE}). USDA-NASS, Washington, DC.
Distribution issues can be directed to the NASS Customer Service Hotline at 1-800-727-9540. Content and technical questions can be directed to the NASS Spatial Analysis Research Section (SARS) at 703-877-8000 or email HQ_RDD_GIB@nass.usda.gov.
We do not offer the data in a vector format, such as shapefile. The Cropscape/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 Cropscape data to a shapefile, but it would have to be a rather small area such as a single county or smaller.
Several users have reported having trouble uncompressing and viewing their downloaded CropScape/CDL data. In every case this was caused by the user trying to use Windows Explorer to view or extract the contents of the zip file. This issue is resolved by using actual WinZIP software (www.winzip.com) to unzip the contents of the zip file. WinRAR or 7-Zip software should also uncompress the downloaded zip file properly.
A user can summarize the area of a certain crop within a certain radius using the CropScape online tools. This can be done at the state, county level, agricultural statistics district (ASD), or region. It is also possible to define your own Area of Interest (AOI) either by using the drawing tools or importing your own shapefile. Here are the basic steps:
1. define an Area of Interest (AOI) - you can do this by state/ASD/county or use the drawing tools to create your own AOI or the Import Area of Interest to use your own shapefile
2. after defining your AOI - click on the 'Area of Interest Statistics' button on the toolbar at the top of the page
3. this brings up an acreage and pixel count summary table
4. if you would like to download a graphic of the area for a single crop or group of crops then you can place a checkmark next to the crop(s) you are interested in
5. click on the 'Export selected crop(s) for mapping' button
6. then click the 'Download' button and specify where to save the file
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 http://www.nass.usda.gov/.
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.
Unfortunately, the pasture and grass-related land cover categories have traditionally had very low classification accuracy in the CDL. Moderate spatial and spectral resolution satellite imagery is not ideal for separating grassy land use types, such as urban open space versus pasture for grazing versus CRP grass. To further complicate the matter, the pasture and grass-related categories were not always classified definitionally consistent from state to state or year to year. In an effort to eliminate user confusion and category inconsistencies the 1997-2013 CDLs were recoded and re-released in January 2014 to better represent pasture and grass-related categories. A new category named Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous (code 171), and Pasture/Hay (code 181). We continue to search for program enhancements and ancillary datasets that may help improve the identification of grassland and pasture categories within the CDL. We recommend users consider using the USGS NLCD (http://www.mrlc.gov/) for research involving non-agricultural categories and grassland/pasture categories.
If your downloaded CDL .tif file does not contain category names, then you can add them using the following instructions. Download the file: generic_cdl_attributes.tif.vat.dbf. This generic file contains all possible CDL colors and category names. As long as the .tif file and the .tif.vat.dbf file have the same file name, then the category names will load automatically in ArcMap. So, change the file name (not extension) of the generic_cdl_attributes.tif.vat.dbf to match the file name of the downloaded CDL .tif file. Then add the .tif file as a layer in ArcMap. The category names will display in the Table of Contents window.
Example 1 - If the downloaded .tif file is: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif Change the generic_cdl_attributes.tif.vat.dbf file name to: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif.vat.dbf
Example 2 - If you renamed the downloaded .tif file to MyCDL.tif, then rename the generic_cdl_attributes.tif.vat.dbf file name to MyCDL.tif.vat.dbf.
To create a pixel "count" field in the attribute table of the downloaded CDL use the "Build Raster Attribute Table" Function in ESRI ArcGIS. In ESRI ArcGIS Version 9.3 and 10 this function is located at ArcToolbox > Data Management Tools > Raster > Raster Properties > Build Raster Attribute Table. Specify the downloaded CDL tif file as the Input Raster and accept all other defaults and click OK. After it has run successfully, a new "Count" data field is added to the attribute table. Count represents a raw pixel count. To calculate acreage multiply the count by the square meters conversion factor which is dependent upon the CDL pixel size. The conversion factor for 30 meter pixels is 0.222394. The conversion factor for 56 meter pixels is 0.774922.
There will be differences between CropScape and official NASS estimates when comparing acreage statistics at the state, district, and county levels. Statistics generated by CropScape are dependent upon pixel counting. 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 http://www.nass.usda.gov/.
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.
The primary focus of the Cropland Data Layer (CDL) is on large area summer crops. The Farm Service Agency CLU data is our primary source of agricultural training data for the CDL classifier. We depend on the data that the farmer reports on their FSA/CLU signup forms. The ground truth is prepared to show whether a single/double crop was planted in a particular field. For example, a winter wheat field planted in the Fall of 2009 will be identified in the 2010 CDL, as we consider the time of harvest as the current year of production. If the field is multi-use during a given year, for example winter wheat (ww) followed by soybeans (sb), then a double cropping situation exists and the category for that given field will be ww/sb, and is indicated as such in the legend. If a field is only sb during that year, then it will be identified as sb only. Therefore, all major crop rotations/patterns are captured with this method and are consider mutually exclusive for a given pixel/field. We do not monitor the fruit and vegetable winter industry (i.e., Florida/California), as we focus primarily on the large area summer crops and are not equipped to monitor triple or quad cropping practices. Please reference the official metadata for a complete list of all possible CDL categories, including valid double-crop categories.
The strength of the CDL is in its agricultural classifications. The major crop types for a CDL state will normally have a classification accuracy of 85% to 95%.
Prior to 2006, the field level training data was collected solely through the June Agricultural Survey (JAS). The JAS is an annual national survey of randomly selected areas of land. The selected areas are targeted toward cultivated parts of each state based on its area frame. Our enumerators are given questionnaires to ask the farmers what, where, when and how much are they planting. Our surveys focus on cropland, but the enumerators record all land covers within the sampled area of land whether it is cropland or not. NASS uses broad land use categories to define land that is not under cultivation, including; non-agricultural, pasture/rangeland, waste, woods, and farmstead making it difficult to know what specific type of land use/cover actually is on the ground. Thus, non-agricultural land cover contained within the 2005 and older CDL products were based solely on an individual analyst's interpretation. Newer CDLs (2006 to current) use agricultural training and validation data provided by the FSA CLU Program. The FSA CLU data does not contain much, if any, non-agricultural data. The only source of non-ag training available at the scale required to meet the needs of the CDL Program is the USGS National Land Cover Dataset (NLCD). We sample the non-ag categories of the NLCD proportionate to the available FSA CLU data for a state and include this in the CDL classification process. Thus, the accuracy of the non-agricultural land cover classes within the Cropland Data Layer are entirely dependent upon the NLCD. We recommend that users consider the NLCD for studies involving non-agricultural land cover. The FSA CLU data does contain a small amount of non-agricultural data and this non-ag FSA data was used in the classification process in early versions of the CDL. Thus, there are some CDL states that may have multiple categories for the same non-ag land cover type, such as category 87 (FSA-sampled wetland) and category 190 and 195 (NLCD-sampled wetlands). This is should only be an issue in the 2006 and 2007 CDL products. Beginning in 2008, the use of the FSA CLU non-ag for classification training was discontinued. Beginning with the 2013 CDL, the use of FSA-sampled grasses and pasture (code 62) was discontinued.All category codes, class names and legend colors are standardized and consistent for all states and all years of the Cropland Data Layer Program.
The 1997-2013 CDLs were recoded and re-released January 31, 2014 to better represent pasture and grass-related categories. A new category named Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous (code 171), and Pasture/Hay (code 181). This was done to eliminate confusion among these similar land cover types which were not always classified definitionally consistent from state to state or year to year and frequently had poor classification accuracies. Please view the 2013 crosswalk document for a detailed listing of the revisions.
This follows the recoding of the entire CDL archive in January 2012 to better align the historical CDLs with the current product. These revisions were done to eliminate redundant and/or unused categories. The majority of the changes apply to the non-agricultural domain. Please view the 2011 crosswalk document for a detailed listing of the revisions.
Some users have reported issues with viewing the downloadable CropScape/CDL GeoTIFF (.TIF) files when using ENVI software. Be sure to keep all associated files (.tif, .tfw, and .aux) together in the same directory. ENVI version 4.4/ENVI Zoom and newer should open the CDL without issue.
A Microsoft Word document was created to provide step-by-step instructions on how to create a CDL legend using ArcGIS. The document is located at: /Research_and_Science/Cropland/docs/CDL_Create_Legend.doc.
In general, no smoothing or filtering is done to the final CDL classification. However, there have been exceptions in the past. The original 2006 CDL products did contain a small level of smoothing, but in March of 2009 all but one of the 2006 CDL products with were re-released with no smoothing. The one exception is the 2006 Washington CDL which still contains the smoothing. Smoothing has also been applied to cranberries in the 2008, 2009, 2010 and 2011 New England States and to oranges in 2008, 2009 and 2010 Florida. Please refer to the "Processing Description" Section of the official metadata files to find out if any smoothing was applied to a particular state or year.
Please visit the Charts and Maps link on the USDA NASS website for information about other spatial datasets. There is also a "Geo Spatial Data" Section located on the right side of that webpage that contains links to more geospatial data. Of particular note are CropScape, VegScape, land use stratification by state, and Crop Progress charts.
The Agricultural Statistics Districts (ASD) for the entire U.S. are available in ESRI shapefile format ASD shapefile. An ASD is defined as a contiguous group of counties having relatively similar agricultural characteristics. The ASD's used by NASS usually divide each state into as many as nine Agricultural Statistics Districts to make data comparison easier. Each district is more homogeneous with respect to agriculture than the state as a whole. The following link provides national State, ASD, and county codes in tabular .csv format asds2009.csv.
Many NASS research reports are available online at the following website: http://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Reports_by_Date/. Many SARS conference presentations are available online at: http://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Presentations/.There is also an online link to a collection of papers by other agencies, universities and private industry that make reference to the CDL available at: "Other CDL Citations".
Distribution issues can be directed to the NASS Customer Service Hotline at 1-800-727-9540. Content and technical questions can be directed to the NASS Spatial Analysis Research Section (SARS) at 703-877-8000 or email HQ_RDD_GIB@nass.usda.gov.
Last Modified: 10/19/2016