by David M. Johnson and Richard Mueller

IntroducƟ on

The National Agricultural Statistics Service 

(NASS) recently released a crop-specifi c land 

cover classification product encompassing 

the entire conterminous United States (U.S.). 

Termed the Cropland Data Layer (CDL), the 

product depicts type and location for crops 

planted during the summer 2009 growing 

season. This effort arises from a confl uence 

of factors including low cost and free midresolution satellite-based imagery, access to 

high quality ground truth, effi cient and robust 

classifi cation software, increased computer 

computational speed and data storage, and 

years of research and development. Spatial 

resolution of the raster-based CDL is 56 meters 

utilizing an Albers equal-area conic projection. 

Pixel-level accuracy for crop type categories 

average around 80 percent but are usually 

much higher for dominant commodity covertypes like corn, soybeans, wheat, cotton, and 

rice. General non-agricultural categories, while 

not the focus, are also mapped. The data are 

available for free and can be integrated and 

analyzed within most geographic information 

systems (GIS).

Background

NASS initiated its research into using remotely sensed data in the 1970s and early 

1980s primarily through participation in the big multi-agency projects known as 

the Large Area Crop Inventory Experiment (LACIE) and Agriculture and Resources 

Inventory Surveys through Aerospace Remote Sensing (AgRISTARS). One goal of 

these programs was to determine if crop acreage estimates could be derived from 

multi-spectral imagery and ground truth data. These early projects were successful at generating land cover classifi cations which were used to derive unbiased 

statistical estimates of crop area at state and county levels. Most importantly 

for NASS, the cropland classifi cations, when intersected with survey indications 

through regression analysis, provided a robust method to reduce statistical variance from farmer reported surveys. This regression estimation methodology is 

usually superior to simple “pixel counting” which is often biased. A comprehensive 

summary of remote sensing use for collecting agricultural statistics is provided 

by Carfagna and Gallego (2005).

The remote sensing acreage estimation research program at NASS has since 

evolved paving the way for the current, more operational, CDL products. It has 

grown, especially over the last few years, in geographic scope from the analysis 

of only a few central states within the U.S. to the entire country. For the core areas 

of Iowa, Illinois, Indiana, North Dakota, and Arkansas there is a decade’s worth of 

annual CDLs produced by NASS. The other Corn Belt and Mississippi River Delta 

region states have a time series dating back to 2006. The remaining lower 48 states 

have been added within the last year or two. Timeliness has also improved with 

data released to the public each January, versus March, after the current season. 

Furthermore, overall quality and accuracy of the CDLs has steadily increased over 

time due to better ground truth and greater access to imagery.

continued on page 1202
1202 November 2010 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Imagery Sources

The primary imagery source for deriving the 2009 CDL is the Indian 

Remote Sensing satellite IRS-P6, also known as Resourcesat-1. It 

was launched in 2003 by the Indian Space Research Organization. The sensor utilized from the platform is the Advanced Wide 

Field Sensor (AWiFS). The design of AWiFS is particularly suited to 

monitoring mechanized large area agricultural production because 

of its relatively wide 737km swath, spectral refl ectance channels 

in the visible, near-infrared and mid-infrared bands, and minimum 

5-day revisit rate. The nadir pixel resolution is 56 meters (about 

0.3 hectares or 0.7 acres) yet detailed enough to identify crop type 

information from fi eld sizes typically found within the U.S. NASS 

has utilized AWiFS data for several years and fi nds it comparable 

to 30 meter Landsat-5 Thematic Mapper (TM) data for agricultural 

monitoring (Johnson, 2008). The resulting trade-off is that AWiFS 

has a coarser spatial resolution but increased temporal frequency 

versus TM (5-day versus 16-day). Because agricultural cover types 

tend to be very dynamic, revisit rate is usually preferred by NASS 

over spatial resolution.

For the 2009 CDL, Landsat 5 TM data were also used to supplement the AWiFS. This was especially true in areas where AWiFS 

coverage was compromised because of a lack of AWiFS collects 

or regions with persistent haze or clouds. Many of the CDLs prior 

to 2006 relied on some, if not all, Landsat data as well. In rare 

cases, where little or no mid-resolution data existed, portions of 

the classifi cation were supplemented with 250 meter data from 

the Terra satellite. In particular, Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day composited Normalized 

Difference Vegetation Index (NDVI) imagery was used. Regardless 

of sensor, most of the imagery was acquired during the summer 

of the current growing season. Input imagery from the spring and 

prior fall was sometimes used as well when available and deemed 

useful. Although not technically imagery, other ancillary data 

sources were also relied upon to further improve the classifi cation. 

Specifi cally, elevation data from the National Elevation Dataset 

(NED) (Gesch et al., 2002) and the forest canopy and imperviousness layers from the National Land Cover Dataset (NLCD) (Homer 

et al., 2007) were used.

Methodology

The CDLs were created using a supervised image classifi cation 

methodology. In short, all of the collected geo-referenced satellite 

imagery and ancillary data were initially “stacked” regionally by 

state within a GIS. Next, samples were taken across the imagery 

stack from areas of “ground truth” which identifi ed the pixel 

locations of specifi c crops. Those sample stacks were then “data 

mined” to determine what set 

of multi-spectral rules from the 

time-series of imagery best 

predicted what land cover category was found at the ground 

truth locations. Finally, once all 

of the classifi cation rules were 

established, all pixels within 

the scene were placed into 

the class they best fit, thus 

building out the state-wide 

classifi cation.

High quality ground truth 

data were the key to driving 

the classifi cation process and 

leveraged through a NASS 

agreement with the Farm Service Agency (FSA). The fi eld 

level information FSA provided 

included spatially detailed 

“Common Land Unit” (CLU) 

field boundary GIS polygon 

data and farmer reported “578” 

administrative data. These 

records were linked by NASS 

and thus created a large and 

timely database of agricultural 

Alfalfa

Cotton

Other Crops

Sorghum

Vegetables/Fruits/ Nuts

Other Small Grains

Rice

Woodland

Shrubland

Urban/Developed

Wetlands

Water

Barren

Perennial Ice/Snow

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continued from page 1201
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING November 2010 1203

continued on page 1204

land use of many farm fi elds throughout the nation. The database was most comprehensive for the major summer grown 

commodity crops and provided a very good sample for training 

of the image classifi er. Of note, neither of the FSA datasets are 

currently available for public access or distribution due to FSA 

confi dentiality laws.

A second source of ground truth data were required to gain 

information about non-agricultural regions since the FSA data 

contained none. The land cover component of the 2001 NLCD was 

chosen as a proxy because of its national coverage and relatively 

up-to-date nature. Ultimately, the NLCD data were sampled at a 

similar proportion as the FSA data while ignoring the “cropland” 

category since it would have been redundant.

The crux of the production and high quality derivation of the 

classifi cation tree rules, was performed by Rulequest Research 

See5.0. It was robust and effi cient and therefore implemented 

versus other methodologies such as maximum likelihood or 

segmentation/object oriented approaches. ERDAS Imagine and 

Environmental Systems Research Institute (ESRI) ArcGIS were used 

for imagery and polygon management, respectively. All software 

and data storage was run on networked Microsoft Windows based 

desktop personal computers in a non-enterprise environment.

Specifi caƟ ons

The CDL product is based on a 56 meter cell-sized grid covering 

the conterminous U.S. The native map projection is Albers equalarea conic, same as used by the NLCD. The data are also available 

in the Universal Transverse Mercator (UTM) projection. File formats 

are GeoTiff (.tif) and Imagine (.img). The CDLs are distributed by 

state but they are “seamless” and can be merged for interstate 

analysis. There are dozens of crop categories within the classifi cation. General non-agricultural categories, analogous to those in 

the NLCD, also exist. The data can be directly read, viewed, and 

analyzed by most commercial GIS and remote sensing software 

packages. Figures 1, 2, and 3 show examples of the CDL mapped 

at national, state, and county level scales, respectively.

State-level accuracy assessment information is available in 

metadata to give the users an idea of the confi dence of each crop 

category’s map accuracy. Pixel level accuracies for the agricultural 

cover types vary by crop type and region. Typically, they are best 

for the most intensive agricultural areas like those found in the U.S. 

“Corn Belt,” and Mississippi River “Delta.” The accuracies for dominant crops like corn, soybeans, wheat, rice, cotton typically exceed 

90 percent. Less common, but still plentiful, crops like potatoes, 

sunfl owers, Canola, and barley average around 80 percent. The 

overall crop accuracy for all states is estimated to be 78 percent. 

States most dominated by intensive cultivation have excellent 

accuracies in excess of 95 percent. The accuracy of non-cropped 

areas are not measured but assumed to be reasonable.

Pasture/Grass

Spring Wheat

Soybeans

Other Hays

Corn

Durum Wheat

Canola

Sunflowers

Dry Beans

Barley

Winter Wheat

Peas

Alfalfa

Fallow/Idle Cropland

Flaxseed

Sugarbeets

Lentils

Oats

Potatoes

Other Crops/Vegs/Fruits

Millet

Safflower

Sorghum

Rye

Seed/Sod Grass

AGRICULTURE

NON-AGRICULTURE

Urban/Developed

Wetlands

Water

Woodland

Shrubland

Barren

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UƟ lity

The ultimate mission of the NASS is, “to provide timely, accurate 

and useful statistics in service to U.S. agriculture”. To achieve this 

goal, NASS conducts hundreds of surveys every year collecting 

information on virtually every aspect of agricultural activity. The 

CDL has now become another component to the agency’s mission. Many of the dominant commodity estimates NASS derives 

from the CDL are used to guide or affi rm in-season survey derived 

acreage indicators established by the NASS Agricultural Statistics Board and Field Offi ces. NASS also actively uses the CDL to 

further refi ne the sampling strategies for its annual fl agship June 

Agricultural Survey. Additionally, the CDLs are a key component 

of NASS crop yield modeling research, and have been used by 

NASS to help isolate by commodity type crop areas that have 

experienced fl ood, hail, or excessive snow.

Outside of NASS the CDL products have been used by a wide 

variety of people desiring agriculture related land cover information for research, agri-business, or environmental applications. 

They include various governments, companies, not-for-profi t 

organizations, academics, scientists, educators, students and crop 

producers themselves. Known uses of the CDL include ground 

truth for other classifi cation efforts, time-series crop phenology 

analysis, fi eld rotation modeling, crop area expansion monitoring, 

ethanol plant locational analysis, epidemiological studies, and 

small area land cover estimation.

Moving Forward

The 2009 CDL is the culmination of much research and development 

but is also believed to be a starting point for a new era of annually 

generated comprehensive and consistent agricultural land cover 

classifi cations. NASS plans to release a new CDL each winter with 

ongoing improvements to resolution and accuracies. The fi rst likely 

near-term change is to push the product to a fi ner spatial resolution as this is known to improve the appearance and accuracy of 

the classifi cation. A 30 meter resolution, which is somewhat of a 

“standard” among GIS datasets such as land cover and elevation, 

will be the fi rst goal. Beyond that it is hoped to increase resolution 

further to a 20 meter product given there are a few sensors already 

currently collecting at that detail and combined are capable of 

providing frequent temporally national coverage. Other ongoing 

research and development improvements to the product include 

better discrimination between agricultural and non-agricultural 

continued from page 1203

1204 November 2010 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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herbaceous cover types, higher accuracies in specialty crops, and 

an earlier release schedule to the public.

The biggest obstacle to the future of the CDL program is 

whether or not there will be the continued access to low cost or 

free mid-resolution, multi-spectral, and multi-temporal imagery. 

Unfortunately, the availability of this type of data in the near future 

is tenuous. It is particularly true in the U.S. due to the aged and 

compromised Landsat missions with a replacement sensor not 

expected until 2013. However, NASS is open to and investigating 

other imagery sources, such as those from commercial or non-U.S. 

programs, and thus it is ultimately hoped future CDL analysis, and 

the value that NASS and others gain from it, will continue.

Acknowledgements

The 2009 CDL was a team effort by Claire Boryan, Mike Craig, 

Lee Ebinger, Mary Lindsey, Marty Saffel, Bob Seffrin, Fred Shore, 

Gail Wade, Patrick Willis, and Audra Zakzeski in addition to the 

authors. Additionally, funding was provided by the U.S. Environmental Protection Agency/Landscape Ecology Branch which 

helped complete 2009 CDL through the purchase of additional 

imagery and staffing time.

Data Access

The 2009 CDL can be downloaded directly from NASS at http://

www.nass.usda.gov/research/Cropland/SARS1a.htm. Older 

products are available at the Geospatial Data Gateway http://

datagateway.nrcs.usda.gov/.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING November 2010 1205

References

Carfagna, E. and Gallego, F. J., 2005. Using Remote Sensing 

for Agricultural statistics, International Statistical Review, 

73 (3): 389-404.

Gesch, D., M. Oimoen, S. Greenlee, C. Nelson, M. Steuck, 

and D. Tyler, 2002. The National Elevation Dataset, Photogrammetric Engineering & Remote Sensing, 68 (1): 5-11.

Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, 

N. Herold, A. McKerrow, J. N. VanDriel, and J. Wickham, 

2007. Completion of the 2001 National Land Cover Database for the Conterminous United States, Photogrammetric Engineering & Remote Sensing, 73 (4): 337-341.

Johnson, D. M., 2008. A comparison of coincident Landsat-5 

TM and Resourcesat-1 AWiFS imagery for classifying croplands, Photogrammetric Engineering and Remote Sensing, 74 (11): 1413-1423.

Authors

David M. Johnson and Richard Mueller

United States Department of Agriculture/National Agricultural 

Statistics Service

3251 Old Lee Highway, Suite 305

Fairfax, VA 22030

dave_johnson@nass.usda.gov, rick_mueller@nass.usda.gov

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