Reported Uses of CropScape and the National Cropland Data 

Layer Program

Rick Mueller

Mark Harris

USDA/National Agricultural Statistics Service/Research and Development Division

3251 Old Lee Hwy, Room 305

Fairfax, VA 22030

Rick.mueller@nass.usda.gov

Mark.harris@nass.usda.gov

ABSTRACT

This paper will highlight the unique cropland area monitoring program which was developed 

within the United States Department of Agriculture’s (USDA) National Agricultural Statistics 

Service (NASS). The report will also focus on the remote sensing based land cover products which 

are delivered via an interactive web portal known as CropScape. CropScape provides open 

accessibility, visualization, and geospatial analytics to the user community. CropScape also 

supports the ethos of data democracy, providing free and open access to digital geospatial data 

layers. CropScape utilizes open web standards, thereby supporting transparent and collaborative 

government initiatives. This paper discusses documented uses and applications of NASS’ Cropland 

Data Layer (CDL) products, and how CropScape, a web-based program, delivered across the 

internet to connect USDA researchers with a previously unreachable audience. CropScape users 

can now interact, visualize, and query CDL products to determine planted agricultural cropland area 

at the field level for any given year across the landscape of our nation.

Keywords: Cropland Data Layer, CropScape, land cover, geospatial

1. Background 

Since 1997, NASS has produced an annual crop-specific land cover product called the Cropland 

Data Layer (CDL). The CDL depicts more than 100 unique crop categories across the United 

States, and is delivered at 30 meters, or .09 hectare pixel resolution. Today, a national CDL product 

is available for the years 2008 through 2012, with the entire historical CDL dataset disseminated via 

CropScape at http://nassgeodata.gmu.edu/CropScape. The CDL is a national land cover product 

delivered just a few months after the growing season concludes. Combined, the CDL and 
CropScape, are unique land cover products and dissemination methods that are not duplicated 

anywhere in the world on a national scale and are performed annually.

The CDL is derived using a supervised land cover classification approach. It combines the 

satellite imagery collected during the growing season from such sensors as Landsat, Resourcesat, 

and the Disaster Monitoring Constellation. It also combines agricultural-specific ground truth data 

from USDA’s Farm Service Agency (FSA) program known as the Common Land Unit (CLU). 

And, it combines ancillary, non-agricultural ground truth data through the United States Geological 

Survey’s (USGS) National Land Cover Dataset (NLCD). The process integration of these satellite 

resources with highly accurate and abundant ground truth provides for this one-of-a-kind, annual 

national land cover data product.

In January 2011, NASS, in cooperation with George Mason University’s Center for Spatial 

Information Science and Systems (CSISS), released CropScape, a new data visualization portal, 

thus enabling open access to NASS’s CDL geospatial data. CropScape was designed to provide 

users with free and open access to interactively visualize, query, and disseminate the CDL by using 

a standard browser interface to extract cropland area statistics, derive charts and graphs, and 

perform change analysis between years. Additionally, CropScape delivers online geoprocessing 

services such as dynamic, mashable, on-demand data delivery which effectively links directly to 

other geospatial applications such as Google Earth.

2. Methods

The CDL program relies on synergistic partnerships with the USDA’s FSA who provides updated 

reported field level data on an annual basis from the CLU data set. This effectuates a robust ground 

truthing set of the agricultural domain. USDA’s Foreign Agricultural Service (FAS) provides 

subscription-based access to their Satellite Image Archive, thus providing independent collections 

of satellite-based data (i.e., Resourcesat or Disaster Monitoring Constellation). The USGS, which 

produces the NLCD (Xian 2009), was utilized to derive the CDL’s non-agricultural domain, along 

with the Percent Tree Canopy, Percent Imperviousness and National Elevation Dataset products. 

The USGS’s Earth Resources Observation Systems (EROS) Data Center has been providing free 

access to Landsat imagery since 2008, and is also used as an image input. The CDL program and 

process to derive the CDL were described in detail (Boryan et al. 2011 and Johnson and Mueller 

2010), utilizing a supervised decision tree classification approach with the aforementioned data 

streams. 

The CDL was initially disseminated via CD-ROM and DVD media in the early 2000’s, and 

subsequently select states became available for download via File Transfer Protocol (FTP) from the 

NASS website (NASS 2013). In the mid-2000’s, USDA’s National Resources Conservation 

Service (NRCS) began serving the entire inventory of CDL products through their Geospatial Data 

Gateway (NRCS 2013). It became apparent that better dissemination methods were needed to meet 

the growing end-users’ need to interact and query this content-rich dataset. 

Hence, a new interactive web-based CDL visualization method was developed to provide access 

like never before to users via web mapping services, while also offering open, geospatial protocols 

for data sharing. CropScape’s primary function (Han 2012) was to offer an interactive, visual, and 

analytical experience that would enable users to download crop area statistics, examine quantitative 

changes on maps between crop years, perform a visual swipe function between years, and print map 

products. This could all be accomplished on a national level, within a state, an agricultural statistics

district, or even within a county. One could also draw their own ad-hoc areas. Since CropScape 

was launched in January 2011, more than 81,000 users have interacted with the site, leveraging its 
comprehensive capabilities with continued demand for enhanced functionality or system 

improvement. CropScape facilitates a dynamic user experience in an open framework for decision 

support, while also upholding numerous agricultural-specific research projects. The remainder of 

this paper focuses on published uses of CDL datasets from peer-reviewed journals and documented 

websites that have ingested CDL data via CropScape mash-ups.

3. Uses of CDL

The CDL product has matured since its creation in 1997, with methods (Boryan et al. 2011), 

ground truth data, and accuracies of cropland identification making vast improvements, while the 

CropScape portal expanded product usage from mostly power Geographic Information Systems 

(GIS) users to those who had little or no GIS-related experience, but had extensive agricultural 

industry knowledge. CropScape has empowered the masses to perform area change and rotational 

crop analysis with a queryable, interactive interface. CDL end-users are now documenting their 

uses in peer-reviewed literature, with uses of the CDL product ranging from research on agricultural 

sustainability studies, to environmental issues, land conversion assessments, crop rotations, decision 

support, disasters, farmer surveys, carbon, bioenergy, ecology, and biodiversity. However, there is 

a segment of our user community who is using it for agribusiness decision support, as well as for 

insurance and financial purposes. These users cannot be quantified and will not publish their uses 

of the CDL dataset, as their uses are considered confidential or trade secrets. Additionally, other 

web portals are developing protocols that either host the CDL data or deliver it as part of web 

mapping services.

The research communities documented uses of CDL range from local food studies in Greenbrier 

Valley, West Virginia, where they are identifying agricultural production areas (Hartz et al. 2011), 

to Montana’s Tongue River Basin, where scientists are studying water resources and availability to 

provide a detailed picture of land use versus agricultural statistics (Fitzgerald and Zimmerman 

2013). And, users at the University of Idaho Extension (Painter et al. 2013) are leveraging 

CropScape to design an oilseed survey that will help producers and processors who were interested 

in trying oilseed production. In another study, the CDL was utilized to identify wheat area, mask

wheat fields, and then derive an empirical regression yield model for Kansas, which was then scaled 

to a wheat-producing region in the Ukraine (Becker-Reshef et al. 2010). Finally, the CDL was 

utilized to characterize crop distributions and changes in crop rotations across the Great Lakes 

Basin (Lunetta et al. 2010).

The following studies focused on land conversions and transitions, where wetlands were 

transitioning to row crops (Johnston 2013), and where western corn belt grassland was being 

converted to corn/soybean production (Wright and Wimberly 2013). Users access the CDL to 

screen results with an agricultural mask and examine the agricultural rotational patterns and fallow 

land for wetland change monitoring in the Northern California marshlands (Potter 2013). Users in 

Illinois and Indiana accessed the CDL to study county level rates of conversion from farmland to 

evaluate developed farmland preservation policies (Thompson and Prokopy 2009). And, 

researchers examining the Northern Great Plains grassland conversions noted the need for 

additional statistical research of such large datasets, and cited CDL accuracy assessments as a 

critical means for deriving parameter estimates in empirical land-use models (Rashford et al. 2013).

The following studies focused on crop rotations, crop area expansion, tilled and cultivation 

studies, and statistical sampling. A study (Plourde et al. 2013) focused on crop rotational patterns, 

with trends moving towards monoculture cropping practices, and examined rotation sequences, 

which reflected the increasing intensification of corn production. The CDL served as a regression 

tree training model for agricultural production trends and crop rotation practices in the Great Platte 
River Basin (Howard et al. 2012). Research on Iowa crop rotation patterns indicated corn 

expansion into land not under recent cultivation, which impacted the amount of soil carbon 

sequestration (Stern et al. 2012). Four years of CDL data were assimilated to create a consistent 

national level annual tilled cropland dataset, and another approach derived a cultivated rule-based 

dataset modeling (Boryan et al. 2012). And, yet another study used the CDL to characterize 

statistical sampling units into strata, to classify land into different types at the field level, and then 

as a proxy to estimate variances for crops of interest (Zimmer 2012). 

The following chemical/environmental studies have used the CDL for project input: sampling site 

selection for pesticide contaminants in High Plains wetlands (Belden et al. 2012); estimating total 

pesticide usage and intensity in the Chincoteague Bay subbasin; characterizing agricultural, 

environmental, and other scientific parameters (Kutz et al. 2010); estimating the watershed scale 

environmental impacts of corn stover removal using the Soil Water Assessment Tool (SWAT) for 

determining that stover removal was sensitive to watershed characteristics and management inputs,

including slope and fertilization applications (Cibin et al. 2012), and estimating field level nitrogen 

losses in another study that merged SWAT predictions with CDL data, thereby tying the changes in 

corn and soybean prices based on the US ethanol mandate to the expansion of the hypoxic zone in 

the Gulf of Mexico (Hendricks et al. 2013).

The CDL has also demonstrated uses in the carbon, bioenergy, ecology, and disaster domains. It 

has been utilized to derive net annual soil carbon changes, calculate the fossil fuel emissions from 

crop production, compute the cropland net primary production, which is used for estimating and 

spatial distributions, and to provide improved estimates of crop carbon dynamics in a spatially 

explicit manner (West et al. 2009). Another study used the CDL to evaluate the potential of

marginal lands for biomass production and mitigation efforts on greenhouse gas emissions (Gelfand 

et al. 2012), and a bioenergy production study (Muth et al. 2013) of comprehensive spatial 

assessments of residue removal across the United States. A unique application was used in a North 

Dakota sunflower field study, which focused on (Schaaf et al. 2009) the foraging use of fall 

migrating non-blackbirds. Finally, scientists utilized CropScape to analyze the crop damage and 

loss following the devastating tornadoes that devoured sections of Alabama in April 2011. Using 

CropScape with tornado swath, along with National Aeronautics Space Administration resources, 

scientists were able to accurately describe crop damage and loss (Herdy 2012).

CropScape provides for a dynamic user experience with wide-ranging capabilities in an open,

geospatial context, and facilitates the delivery and analysis of geospatial cropland information to the 

user community. Currently no other entity provides an annual national crop specific land cover 

product in an interactive, accessible manner, via a visually immersive experience. CropScape’s 

Open Geospatial Consortium compliant architecture and web mapping services have created 

opportunities from within the geospatial community to develop data sharing through the wire, 

encapsulating one website’s geospatial data within another, using a technique called mash-up (Han 

et al. 2012). 

Many sites utilize web-based tools for the United States cropland, bioenergy, and soils data 

exploration, including the demonstration of linking CDL spatial functionality to detailed crop 

rotation management for agroecosystem modeling (Kipka et. al 2013) using web service requests. 

The Billion Ton Study (US Dept. of Energy 2011) hosts a mashable site called the Knowledge 

Discovery Framework at https://bioenergykdf.net/ where varying scales of data i.e., national, 

regional, and local can be queried, analyzed, uploaded, and visualized for decision-support with 

regard to economic and environmental impacts of development options for biomass feedstock 

production and biorefinery infrastructure. Another site where the CDL can be queried against 

bioenergy datasets for acreage planted information (Yang et al. 2011) was developed in another 

application called integrated Agricultural Information and Management System (iAIMS), and it 
delivers dynamic and interactive climate, soil, CDLs, and road infrastructure databases at 

https://beaumont.tamu.edu/CroplandData/.

4. Discussion

In preparation for this paper, Google Analytics were run against CropScape’s user base, and the 

following information was tabulated. Figure 1a depicts CropScape’s global user base, with the 

majority of users coming from the United States. However, the reach of CropScape was global with 

users reporting from every continent except Antarctica. Table 1 shows the number of top ten

country unique visitors in descending order using CropScape with the United States, Canada, China, 

Germany, United Kingdom, Argentina, France, Brazil, Spain, and Mexico. Figure 1b shows the 

United States locations of CropScape users; California, Illinois, Minnesota, Virginia, and Iowa were 

the top five reported state users, in descending order. Note that both the CSISS and NASS Spatial 

Analysis Research Section were located in the state of Virginia, which could account for higher 

than expected numbers. The total number of unique reported visitors was 81,650 as of June 2013. 

Figure 2 shows the reported number of CropScape visitors since CropScape became operational in 

January 2011. Note the sharp user access increase during both January 2012 and 2013, when the 

newest CDL products were released. However, there was an intense user spike for unforeseen 

reasons during the end of June 2011, when users visited CropScape coincident with the release of 

the NASS June Acreage Report. It is believed that the public thought that the CDL was going to be 

updated and released coincident with the June Acreage Report. However, it is impossible to release 

a completed, final CDL product at this time, as it is necessary to wait until the growing season has 

completed, all crops have emerged, matured, and senesced. Once that is complete, the metadata is 

finalized, and all products are checked for quality assurance and quality control.

Figure 3a shows the CropScape website and interface at http://nassgeodata.gmu.edu/CropScape. 

CropScape provides additional ancillary data layers for purposes of navigation and dissemination, 

including a crop mask to shade-out all non-agricultural areas, and political, water, and road 

boundary layers to facilitate orientation purposes. Figure 3b shows an enlarged depiction of 

Craighead County, Arkansas in 2012, with the crop legend appearing on the left side of the graphic. 

Note there was much discussion and many decisions to be made to properly represent each crop 

category cartographically. When making these decisions, we considered each crop category color

and its geographic distance to similar crop colors. Mapping the more than 100 crop categories was 

extremely challenging (Ebinger 2012).

1a. Global CropScape users. 1b. US CropScape users.

Figure 1a: The darker blue color indicates higher incidence of reported CropScape users globally. 

Figure 1b: The darker blue color indicates higher incidence of reported CropScape users in the

US.

Country Unique Visitors 
US 73,303

Canada 1,100

China 913

Germany 502

United Kingdom 441

Argentina 393

France 388

Brasil 362

Spain 300

Mexico 291

Table 1: List of the top ten unique national country visitors to CropScape, note n = 81,650.

Figure 2: Lists the unique number of unique visits since January 2011 when CropScape became 

operational.

Figure 3a: CropScape website interface, note the CDL years of data available.
Figure 3b: A zoom enlargement of Craighead County, Arkansas 2012. The crop legend appears 

on the left side of the graphic.

5. Conclusion

The combinations of the CDL product disseminated with CropScape provide a unique one-of-akind, interactive, visual, and analytical experience. The ethos of data democracy were upheld,

providing free and open access to digital geospatial data layers in an open standard web format,

supporting a transparent and collaborative government initiative. Users can query, compute 

statistics, perform change analysis, map, and visualize the entire inventory of CDL data derived at 

30m or .09 hectares resolution and show field level accuracy. The CDL is now a national level 

product, reproduced annually and publically disseminated just a few months upon completion of the 

growing season. The reported uses of CDL and CropScape in published peer-reviewed journals and 

literature were discussed. 

The uses of CDL data have grown, where users leverage it for agricultural decision support and 

scientific research. And finally, perhaps the most successful use of CropScape and the CDL were 

related websites that have developed protocols whereby they either host CDL data or deliver it as 

part of web mapping services. That is the ultimate mash-up of technology and product success.

REFERENCES

Becker-Reshef, Inbal, Vermote, E., Lindeman, M., and Justice, C., (2010). A generalized 

regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS 

data. Remote Sensing of Environment, 114(6): pp. 1312-1323.

Belden, Jason, Hanson, B., McMurry, S., Smith, L., and Haukos, D., (2012). Assessment of the 

effects of farming and conservation programs on pesticide deposition in High Plains wetlands.

Environmental Science & Technology 46(6): pp. 3424-3432.
Boryan, Claire, Yang, Z., and Di, L., (2012). Deriving 2011 Cultivated Land Cover Data Sets 

Using USDA National Agricultural Statistics Service Historic Cropland Data Layers. Proc. 

IGARSS 2012 Conf., Munich, Germany, July 23-26, 2012.

Boryan, Claire, Yang, Z., Mueller, R., and Craig, M., (2011). “Monitoring US Agriculture: The US 

Department of Agriculture, National Agricultural Statistics Service Cropland Data Layer 

Program” Geocarto International 26(5): pp. 341 – 358.

Cibin, R., Chaubey, I., and Engel, B., (2012). Simulated watershed scale impacts of corn stover 

removal for biofuel on hydrology and water quality. Hydrological Processes 26 pp. 1629 – 1641.

Ebinger, Lee, (2012). 133 map categories! How the US Department of Agriculture solved a

complex cartographic design problem. Wisconsin State Cartographers Office. Available from 

http://www.sco.wisc.edu/news/133-map-categories-how-the-us-department-of-agriculture-solved-acomplex-cartographic-design-problem.html. (Accessed 30 June 2013).

Fitzgerald, Timothy, and Zimmerman, G., (2013). Agriculture in the Tongue River Basin: Output, 

Water Quality, and Implications. Agricultural Marketing Policy Paper No. 39, pp. 1 – 41.

Gelfand, Ilya, Sahajpal, R., Zhang, X., Izaurralde, R., Gross, K., and Robertson, G., 2013. 

Sustainable bioenergy production from marginal lands in the US Midwest. Nature 493, pp. 514 -

517. 

Han, W., Yang, Z., Di, L., and Mueller, R., (2012). CropScape: A Web service based application 

for exploring and disseminating US conterminous geospatial cropland data products for decision 

support. Computers and Electronics in Agriculture Vol. 84, June, pp. 111– 123.

Hartz, Laura, Boettner, F., and Clingerman, J., 2011. Greenbrier Valley Local Food: The 

Possibilities and Potential. Greenbrier Valley Economic Development Corp. pp. 1 – 34.

Hendricks, Nathan, Sinnathamby, S., Douglas-Mankin, K., Sumner, D., and Earnhart, D., (2013). 

The Environmental Effects of Crop Price Increases: Nitrogen Losses in the U.S. Corn Belt. Inpress.

Herdy, Claire, Luvall, J., Cooksey, K., Brenton, J., Barrick, B., Padgett-Vasquesz, S., (2012). 

Alabama Disasters: Leveraging NASA EOS to explore the environmental and economic impact 

of the April 27 tornado outbreak. 5th Wernher von Braun Memorial Symposium; Huntsville, 

AL. pp. 1-9.

Howard, Daniel, Wylie, B. and Tieszen, L., (2013). Crop classification modelling using remote 

sensing and environmental data in the Greater Platte River Basin, USA. International Journal of 

Remote Sensing, 33(19): pp. 6094-6108. 

Johnson, David, (2013). A 2010 map estimate of annually tilled cropland within the conterminous 

United States. Agricultural Systems 114: pp. 95–105.

Johnson David and Mueller R. 2010. The 2009 cropland data layer. Photogrammetric Engineering 

& Remote Sensing 76(11): pp. 1201 – 1205.

Johnston, Carol, (2013). Wetland Losses Due to Row Crop Expansion in the Dakota Prairie 

Pothole Region. Wetlands, 33: pp.175-182.

Kipka, Holm, David, O., Lyon J., Garcia, L., Green T., Ascough II, J., and Rojas, K., (2013). A 

web-service based tool to generate crop rotation management input files for spatially distributed 

agroecosystem models. Hydrology Days pp. 38 – 46.

Kutz, Frederick, Morgan, J., Monn, J., and Petrey, C., (2012). Geospatial approaches to 

characterizing agriculture in the Chincoteague Bay Subbasin. Environmental Monitoring and 

Assessment, 184(2): pp. 679-692.

Lunetta, Ross, Shao, Y., Ediriwickrema, J., and Lyon J., (2010). Monitoring Agricultural Cropping 

Patterns across the Laurentian Great Lakes Basin Using MODIS-NDVI Data. International 

Journal of Applied Earth Observation and Geoinformation, 12: pp. 81-88.

Muth Jr., D.J., Bryden, K., and Nelson, R., (2013). Sustainable agricultural residue removal for 

bioenergy: A spatially comprehensive US national assessment. Applied Energy 102, pp. 403-417.

NASS (2013). US Department of Agriculture, National Agricultural Statistics Service, Spatial 

Analysis Research Section. Available from: 

http://www.nass.usda.gov/research/Cropland/SARS1a.htm, (accessed June 27, 2013).
NRCS (2013). US Department of Agriculture, National Resources Conservation Service. 

Geospatial Data Gateway. Available from http://datagateway.nrcs.usda.gov, (accessed 27 June 

2013).

Painter, Kathleen, Donlon, H., and Kane, S., (2013). Results of a 2012 survey of Idaho oilseed 

producers. Agricultural Economics Extension Series. No 13-01. 

Plourde, James, Pijanowski, B., and Pekin, B., (2013). Evidence for increased monoculture 

cropping in the Central United States. Agriculture, Ecosystems & Environment, 165(15): pp 50-

59.

Potter, Christopher, (2013). Ten years of vegetation change in Northern California marshlands 

detected using Landsat satellite image analysis. Journal of Water Resource and Protection, 5:

pp. 485-494.

Rashford, Benjamin, Albeke, S., and Lewis, D., (2013). Modeling Grassland Conversion: 

Challenges of Using Satellite Imagery Data. American Journal Agricultural Econonomics,

95(2): pp. 404-411.

Schaaf, Dionn, Linz, G., Doetkott, C., Lutman, M., and Bleier, W., (2008). Non-blackbird Avian 

Occurrence and Abundance in North Dakota Sunflower Fields, The Prairie Naturalist 40(3/4): 

September/December. 

Stern, Alan, Doraiswamy, P., and Hunt, R., (2012). Changes of crop rotation in Iowa determined 

from the United States Department of Agriculture, National Agricultural Statistics Service 

cropland data layer product. Journal of Applied Remote Sensing, 6(1): pp. 1- 16.

Thompson, Aaron and Prokopy, L., (2009). Tracking urban sprawl: Using spatial data to inform 

farmland preservation policy. Land Use Policy, 26(2): pp. 194-202. 

U.S. Department of Energy. (2011). U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and 

Bioproducts Industry. R.D. Perlack and B.J. Stokes (Leads), ORNL/TM-2011/224. Oak Ridge 

National Laboratory, Oak Ridge, TN. 227p.

West, Tristram, Brandt, C., Baskaran, L., Hellwinckel, C., Mueller, R., Bernacchi, C., Bandaru, V., 

Yang, B., Wilson, B., Marland, G., Nelson, R., De La Torre Ugarte, D., and Post, W., (2010). 

Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based 

carbon accounting. Ecological Applications 20(4): pp. 1074–1086.

Wright, Christopher, and Wimberly, W., (2013). Recent land use change in the Western Corn Belt 

threatens grasslands and wetlands. Proceedings of the National Academy of Sciences (USA), 

110(10): pp. 4134-4139.

Xian, G., Homer, C., Fry, J., (2009). Updating the 2001 National Land Cover Database land cover 

classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of 

Environment, 113(6), 1133-1147.

Yang, Yubin, Wilson, L., Wang, J., and Li, X., (2011). Development of an Integrated Cropland and 

Soil Data Management System for Cropping System Applications. Computers and Electronics in 

Agriculture, 76: 105-118. 

Zimmer, Stephanie, Kim, J., Nusser, S., (2012). A hierarchical clustering algorithm for multivariate 

stratification in stratified sampling. Joint Statistical Meetings, San Diego, CA pp. 1-11.