The USDA NASS Cropland Data Layer Program

Transition from Research to Operations

(2006-2009)

Claire G. Boryan, Geographer

United States Department of Agriculture

National Agricultural Statistics Service

May 10, 2010
2

1. INTRODUCTION

The mission of the National Agricultural Statistics Service (NASS), an agency of the 

United States Department of Agriculture (USDA) is “to provide timely, accurate and useful 

statistics in service to U.S. agriculture”. Towards this goal, NASS conducts hundreds of surveys 

every year collecting information on virtually every aspect of agricultural activity. In 2009, the 

NASS Cropland Data Layer Program played an important role toward fulfilling this mission using

remote sensing techniques to provide operational in-season acreage estimates to the NASS 

Agricultural Statistics Board (ASB) and Field Offices (FOs) for twenty-seven states and fifteen 

crops. 

The Cropland Data Layer product (Fig. 1) is a comprehensive, raster, geo-referenced, cropspecific land cover classification with a ground resolution of 56 meters, which utilizes satellite

imagery to accurately locate and identify field crops. For the first time in 2009, these freely 

available crop-specific Geographic Information System (GIS) data layers were created for all 

forty-eight contiguous United States. These GIS products are valuable resources for government 

agencies, private sector organizations, scientists, educators and students that use land cover 

information for environmental, agricultural, business or research purposes. 

Figure 1. The USDA NASS Cropland Data Layer

The Cropland Data Layer products have been used in a variety of research applications 

which include assessing the utility of 500 meter (m) Moderate Resolution Imaging 

Spectroradiometer (MODIS) Time-Series Data for mapping corn and soybean in the U.S. (Chang 

et al., 2007), validating plant functional type maps developed from MODIS data using multisource 
3

evidential reasoning (Sun, et al., 2008), examining the relationship between agricultural chemical 

exposure and cancer (Maxwell, et al., 2010) and flood mapping assessment with satellite images 

(Shan, et al., 2010). The Cropland Data Layer was also used to assess the utility of using high 

resolution aerial imagery to monitor tree cover in agricultural landscapes in North and South 

Dakota (Liknes, et al., 2010) and to assess automated determination of management units for 

precision soil conservation (Gelder, et al., 2008). 

2. BACKGROUND

NASS initiated its remote sensing acreage estimation program, in the 1970s and early 

1980s, with the Large Area Crop Inventory Experiment (LACIE) and Agriculture and Resources 

Inventory Surveys through Aerospace Remote Sensing (AgRISTARS). The objective of these 

programs was to determine if crop acreage estimates could be derived using multispectral imagery 

and ground reference data. These programs were successful at generating unbiased statistical 

estimates of crop area at the state and county level and, more importantly, reducing the statistical 

variance of acreage indications from farmer reported surveys (Craig, 2009). The NASS remote 

sensing acreage estimation program evolved over the years, paving the way for the current

Cropland Data Layer (CDL) program, which has been in existence since 1997. 

Cropland Data Layer image products and acreage estimates were originally produced using 

Landsat imagery, NASS survey data, and an in-house remote sensing and estimation software 

known as Peditor. NASS and the University of Illinois Center for Advanced Computing developed 

a customized program called Editor. It was transferred to other computer platforms by NASS and 

the name was modified to Peditor. The historic Peditor method delivered state- and county-level 

indications in late December for the Crop Production Annual Summary (Craig, 2009). 

Beginning in 2006, the CDL program underwent a major restructuring and modernization 

effort. The original software and data inputs were replaced with a commercial suite of software 

including Rulequest Research’s See5 decision tree software, ERDAS Imagine 9.1 remote sensing 

software, Environmental Systems Research Institute’s (ESRI) ArcGIS, Statistical Analysis 

Software, Resourcesat-1 Advanced Wide Field Sensor (AWiFS) data, and Common Land Unit 

data from the Farm Service Agency (FSA). The tremendous efficiency gains resulting from the 

modernization of the CDL program allowed for the generation of in-season crop acreage estimates, 

which was previously an unattainable goal.

This paper describes the transition of the CDL program from research to operations,

providing in-season acreage estimates in 2009 to meet NASS production deadlines for twentyseven states and fifteen crops multiple times during the growing season. This overview will 

describe the original CDL method; the factors leading to change; the emergence and use of 

administrative data as a source of ground reference training and validation data; the shift from

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) to Resourcesat-1
4

for satellite imagery; the current method and software; the scope, timing and delivery of products; 

accuracy; and CDL image product delivery.

3. FACTORS LEADING TO CHANGE

The major drawback of the original CDL method, which used Peditor and Remote Sensing 

Program software, Landsat data and NASS’s June Area Survey (JAS) segment data as ground 

reference data, was the time required to generate the CDL image products and acreage estimates. 

From 1997-2006, estimates were delivered to NASS headquarters by mid-December of each year 

for consideration in setting the official county estimates for major “Program Crops”, which are 

those crops for which famers receive subsidies. Over these years, the number of states and crops 

included in the remote sensing estimation program grew but crop acreage estimates could not be 

obtained by October 1, which is the deadline for consideration in the October Crop Production 

Report. Nonetheless, the CDL acreage estimates were used at the state level as supplementary 

indications, after the fact, and at the county level for operational purposes. By making changes to 

the CDL program to increase the efficiency, in-season estimates of major crops could be provided

to the NASS Agricultural Statistics Board and the state field offices. Furthermore, a still larger 

goal of the CDL program was to not only provide state-level acreage estimates to the Agricultural 

Statistics Board in October, but to also meet all estimation deadlines beginning with the June Crop 

Acreage Report through the December Crop Production Report.

4. GROUND REFERENCE DATA

Both NASS and FSA collect field-level crop information. NASS collects the JAS segment 

data and FSA collects Common Land Unit polygon data as illustrated in Fig. 2. The scope of the 

FSA Common Land Unit (CLU) program is comprehensive including all states and extensive 

coverage of major crops. The program is run at the county level in over 2,300 FSA county offices. 

There are two important differences between using NASS JAS and FSA CLU data as ground 

reference training data in the CDL program. First, the individual polygon boundaries of the JAS

segments are regularly digitized to support the survey, but the individual fields within each 

segment would require additional digitizing, a time consuming and labor intensive process. The 

FSA CLU polygon data, on the other hand, are manually digitized and crop-specific attribute data 

are collected in the FSA county offices, as part of a standardized program that collects information 

on all fields included in the FSA programs for compliance and administration purposes (Mueller 

et al., 2009; Heald, J., 2002). A second difference is that the coverage of major crops provided by 

FSA is more comprehensive than the JAS segments. In fact, the FSA CLU data approximates full 

coverage in large production states

There are several shortcomings to using the FSA data. First, a relatively large proportion 

of CLU polygons include more than one crop type, while JAS segments are digitized to the field

with only one crop represented (Craig, 2005). In order to use the FSA data, CLUs with mixed 

crop types, except certain double crops such as winter wheat followed by soybeans, are excluded 
5

from the ground reference data. Second, specialty crops are not well represented in the FSA data 

leading to a bias toward major speculative crops, which are those for which farmers received 

subsidies. Third, farmers do not report the crops grown in all CLU polygons each year (Craig, 

2001; Mueller et al., 2009). 

Figure 2. FSA CLU polygon data 

Fortunately, these shortcomings are overshadowed by the sheer volume of crop data 

available from the FSA CLU program. Being a comprehensive agricultural data set that requires 

minimal preparation and can be updated multiple times, during the growing season, to include the 

most accurate and recent farmer reported data, greatly outweighs the disadvantages. Using the 

FSA CLU and 578 attribute data for training has dramatically increased the volume and timeliness 

of available ground reference data and thereby increased the scope, efficiency and accuracy of the 

operational CDL program.

5. SATELLITE IMAGERY 

In the late 1990s, NASS used both Landsat TM and ETM+ data with a 30 meter spatial 

resolution in CDL production. The Landsat sensors have a 185 km swath; seven spectral bands 

including a visible blue, visible green, visible red, near infrared red (NIR), two mid infrared (MIR) 

bands and a thermal band; a 16-day repeat and eight bit quantization. The synchronization of the 

two sensors to achieve an eight-day repeat cycle was appropriate for acquiring crop information 

during the growing season. Landsat data were purchased and made available to NASS via the 

USDA’s Foreign Agricultural Service (FAS), which established the satellite image archive for the 

purpose of coordinated purchases of satellite imagery for the entire Department of Agriculture

(Craig, 2009).

On May 31, 2003, the Landsat 7 ETM+ sensor experienced an anomaly in its scan line 

corrector. At the time, the imagery was considered unusable by NASS and the CDL program 

experienced a 50% reduction in the inventory of available satellite imagery. In 2004, the USDA 

purchased imagery, for evaluation purposes, from the Indian Remote Sensing Satellite (IRS) 
6

RESOURCESAT-1 launched in October of 2003. The moderate spatial resolution (56 meter) 

Advanced Wide Field Sensor (AWiFS) data were selected for evaluation as a substitute for Landsat 

imagery in CDL production. The AWiFS imagery was found effective for crop acreage estimation 

(Boryan and Craig, 2005; NASS, 2006; Seffrin, 2007; Johnson, 2008) after which time NASS 

decided along with its partner, the FAS, to purchase AWiFS data exclusively for the USDA’s 

satellite imagery archive.

In 2006, NASS began using AWiFS data as the primary source of imagery. The AWiFS

sensor offers a moderate spatial resolution (56 meter), a large swath width (720 km), appropriate 

spectral characteristics for agriculture monitoring and a rapid revisit (5-day repeat) capability. The 

56-meter spatial resolution, though coarser than Landsat’s 30 meters is sufficient for the accurate 

identification of large homogenous crop fields (NASS, 2006). Additionally, the full swath width 

of 720 kilometers, when using both camera A and B acquisitions, provides an excellent opportunity 

for large area coverage with single day acquisitions. AWiFS offers four spectral bands that closely 

resemble the most useful of Landsat 5 TM and Landsat 7 ETM+. The sensor acquires data in the 

visible green, visible red, NIR and short wave infrared (SWIR) bands. The 5-day temporal 

resolution of AWiFS is a significant improvement from the 16-day revisit of Landsat 5 TM 

providing the opportunity for abundant nearly cloud free imagery collected throughout the growing 

season.

Figure 3. IRS Resourcesat 1 – Advanced Wide Field Sensor Imagery acquired on August 

2, 2009. Acquisition descriptions include path/row/quad information. The brightly colored 

quads are those used in CDL processing.

From the 2006 - 2008, AWiFS imagery was collected from April 1 through the month of 

October, which is the summer growing season. Acquisitions were excluded based on a 50% cloud 

cover criteria. With the transition to See5 Decision Tree Software, NASS was able to use a large 

volume of satellite imagery spanning multiple dates to create classifications. This expansion in 

the number of scenes acquired throughout the growing season allowed for better separation of crop 
7

types based on the varying crop phenological cycles. In 2006, Moderate Resolution Imaging 

Spectroradiometer Data (MODIS) 16-day Normalized Difference Vegetative Index (NDVI) 

composites began to be used in the classification process. With its 250-meter spatial resolution, 

MODIS data could not replace AWiFS data but was useful when collected during the late fall over 

specific states where the winter wheat crop was beginning to emerge. These fall collections were 

used to estimate winter wheat acreage in large producing states to support JAS estimation. 

In 2009, NASS regularly supplemented AWiFS data with Level 1T (terrain corrected) 

Landsat 5 TM and Landsat 7 ETM+ data for CDL production, as the entire USGS Landsat Data 

Archive became available for public consumption at no charge (USGS, 2010). The Landsat data 

were downloaded from Glovis (https://glovis.usgs.gov). Post processing steps included converting 

the data from GeoTIFF to ERDAS Imagine image (.img) format, reprojecting from Universal 

Transverse Mercator to Albers Conical Equal Area, resampling from 30 to 56 meters using cubic 

convolution resampling method, and mosaicing same day acquisitions. 

During the 2009 CDL season, AWiFS experienced technical problems including an onboard data recorder failure and degraded solar panel capacity. Furthermore, increased competition 

from international customers reduced the availability of AWiFS data for purchase over the United 

States by the Foreign Agricultural Service Satellite Imagery Archive. Fortunately, the freely 

available Landsat data could be used as a source of supplemental imagery. The CDL program 

would not have been able to meet all program deadlines and expand its scope to include the forty 

eight conterminous states without the use of Landsat data.

6. REMOTE SENSING CLASSIFICATION SOFTWARE

In 2004, transitioning the CDL program from research to operational status appeared to be 

in the realm of possibility. Changes including new imagery, ground reference data, and image

processing and estimation software were required. Already in place were the FSA CLU data, which 

provided an expansive source of agricultural ground reference data and required no in-house 

digitization, a significant advance. Additionally, the JAS segment boundaries could still be used 

as an independent data source for regression modeling. Also available were the AWiFS data, which 

showed promise for large area coverage at a 5-day repeat cycle. The next step was the identification 

of commercial remote sensing software that could perform the functions of the Peditor maximum 

likelihood classifier software.

 NASS evaluated ERDAS Imagine, Definiens’ eCognition and Rulequest Research’s See5 

decision tree software. The remote sensing software selected needed to be affordable, efficient and 

accurate. See5 came highly recommended by EROS Data Center researchers and was used to 

produce the National Land Cover Database for 2001 and through literature reviews (Homer, C. et 

al., 2004; 2007; Hansen et al., 1996; Friedl and Brodley, 1997 and Lawrence et al., 2004) was 

found to be the most appropriate replacement for the Peditor maximum likelihood classifier. 
8

See5 was the remote sensing classification software used by NASS from 2006 -2009 and 

was the primary driver of the expansion of the CDL program. The most significant difference 

between Peditor and See5 was the time required to produce a statewide CDL. Once the See5 

method was fully developed, an experienced analyst could produce a statewide CDL, after all 

preprocessing of ground reference data, imagery, and ancillary data was complete, within several 

days. It required between one to two months for the same analyst, using the Peditor method, to 

produce a statewide CDL product. The difference was in large part because See5 was able to 

generate a statewide CDL in one process incorporating all input data. 

Although Peditor was an excellent classifier, a number of limitations made the 

classification process more time consuming. Peditor operated by creating multiple smaller 

classifications. The intersection of same date Landsat scenes defined “analysis districts”. A 

separate classification was generated for each analysis district. Using the Peditor method, some 

states required as many as twelve separate analysis districts, which in turn required running twelve 

separate classifications to produce a statewide CDL. The individual classifications were merged 

to create the statewide CDL mosaic. 

With See5, even though by definition it classifies the intersection of inputs, there is a 

technique to get around this obstacle so that the entire state or region can be classified in one 

process. All input data including imagery and ancillary data must be set to a specific map extent 

when created. Consequently, even though all of the imagery does not cover an entire state, if all of 

the inputs are set to this specific map extent, then See5 categorizes all land cover within this region

or state. While it takes additional time preparing the input data, the amount of time saved in the 

classification phase is significant.

Additionally, See5 provides options that improve the quality and accuracy of the CDL 

products. These options include allowing for the ingestion of an abundance of satellite imagery 

and other non-parametric data sources; incorporating a boosting algorithm in which the classifier 

reviews the results multiple times to refine or “prune” the decision tree; tolerating image noise, 

such as clouds, haze or even gaps in the imagery and generating confidence layers that

corresponded to the resulting classifications. Lastly, the National Land Cover Database (NLCD)

2001 can be used with See5 for training on non-agricultural categories and can be combined with 

the agricultural training to create a complete training set for the state or region.

In 2009, NASS used AWiFS, MODIS, Landsat TM and ETM+ data to produce the CDL 

products. Imagery was acquired from the fall of 2008 until late September 2009. Using imagery 

collected over the entire growing season facilitated the separation of crop phenologies and the 

accurate identification of cropland. In some instances, over a particular area, six or more satellite 

scenes acquired during the growing season were used to classify the land cover. This was 

extremely useful when attempting to identify double crops such as winter wheat followed by 

soybeans or crops with similar phenologies. Peditor could only ingest a maximum of two scenes

of a study area, a significant limitation. 
9

Nonparametric data sets such as the United States Geological Survey (USGS) Digital 

Elevation Model, USGS percent canopy layer, and USGS percent impervious layer were used from 

2007 – 2009 to help identify non- agricultural categories and separate them from crops. The USGS 

Digital Elevation Model is most useful in regions with significant topographic variation. Further, 

crops are most often grown in areas of low topographic relief. For example, in Mississippi, 

Louisiana and Missouri, a significant percentage of the agriculture grown in the region is located 

in the low lying portion of the Delta. The percent canopy layers help identify the forested areas 

and the percent impervious layers helps identify urban infrastructure. These raster layers could not 

be used with Peditor. 

Boosting or bagging, in which the classifier reviews the results multiple times to refine or 

“prune” the decision tree, was available with See5. This was shown to improve accuracy in the 

literature (Quinlan, J., 1996). In 2009, ten boosts were generally run to refine the CDL 

classification. Boosting was not available with Peditor. 

The NLCD 2001 is currently used for training for non-agricultural categories. The NLCD 

2001 was released in 2006 at which time NASS began using it for non-agricultural sampling. When 

using Peditor, an analyst had to manually create non-agricultural ground reference training data. 

“Extra signatures” were created for clouds, water, grass, trees, wetlands and many other nonagricultural categories, a very labor-intensive process. Additionally, these “extra signatures’ were

created for each individual classification or analysis district with Peditor. 

A tremendous advantage of See5 and improvement in operational efficiency was the 

software’s tolerance of image noise such as clouds, haze and the scan gaps in the Landsat 7 ETM+ 

data. As long as there was an abundance of clear imagery overlaying the same location as the 

image noise, the software seemingly ignored the bad data. When using Peditor, “extra signatures” 

had to be created for all analysis districts in which clouds were evident.

7. REMOTE SENSING PROGRAM SOFTWARE TO ESRI ARCGIS

Starting in 2006, when the FSA CLU data became the primary source of ground reference 

data for the CDL program, the switch was made from Remote Sensing Program (RSP) software to 

ESRI’s ArcGIS software. ArcGIS was the clear choice as USDA had an enterprise software license 

and many staff were trained in its use. The preparation of the FSA CLU data was dramatically

more efficient when using ArcGIS than it was with RSP software. 

Models were written in ArcGIS to merge the original county FSA CLU shape files into 

statewide shape files. The shape files were then cleaned, reprojected to Albers Conical Equal Area

projection and buffered inward 56.0 meters. All of these steps, which were relatively time 

consuming for 48 states, were completed on the CLU polygon data in 2009, prior to the start of 

the growing season. All of these processes could not be performed with RSP, which was primarily 

used for digitizing and editing crop attribute information. During the crop season, the JAS segment 
10

data required approximately one month for digitizing in the NASS Field Offices and two weeks 

for editing by an analyst.

Once the FSA CLU polygons were linked to the FSA 578 attribute data, ArcGIS models 

were used to exclude non-matching CLUs, separate CLUs into training and validation data sets, 

and rasterize the shape files for use in See5. The ArcGIS models dramatically improved the 

efficiency of the process whereby the most current ground reference training data could be used 

prior to in-season deadlines. ESRI’s ArcGIS was an important contributor improving the 

efficiency and quantity of the ground reference data available for use in producing the CDLs.

8. PEDITOR TO STATISTICAL ANALYSIS SOFTWARE 

From 1997 to 2005, Peditor performed all of the functions of both remote sensing 

classification and estimation software system. Once the decision was made to transition from 

Peditor to See5, new estimation software was required. Statistical Analysis Software (SAS) was 

selected as it was widely used within NASS and had the statistical analysis capabilities that NASS 

required. In 2006, the regression estimator in Peditor was well developed and documented (Day, 

2002). Consequently, the identical programs written and run in Peditor were transitioned to SAS.

By 2007, SAS was able to increase the efficiency of estimation modeling and help 

transition the CDL program from research to operational status. One of the most important 

advantages of SAS was the ability to interactively review the regression analysis results in IML 

Workshop (called Stat Studio in SAS 9.2). This made the process of removing outliers and 

rerunning the regression modeling less labor intensive for statisticians. Second, the original format 

of JAS segment data was formatted in SAS and made the data easier to use. Third, result tables in 

SAS were output in PDF files and Excel files, which were easier for NASS Headquarters

statisticians to import and analyze than the ASCII` tables generated in Peditor. Another important 

advantage that occurred during the transition from Peditor to See5 and SAS was the ability to run 

estimates for the entire state at one time. 

9. SCOPE AND TIMING 

NASS’s Cropland Data Layer program has been in existence since 1997. For the first 10 

years, Peditor was the software of choice as no other existed that could run remote sensing 

classifications and regression modeling. During this ten- year period, the program expanded from 

two states in 1997 to nineteen states in 2006 delivering state and county estimates to the NASS 

Agricultural Statistics Board in mid-December of each year for consideration in setting countylevel estimates. With the transition of the program to a commercial software suite; including See5, 

ArcGIS, ERDAS Imagine, and SAS; tremendous technological and methodological efficiencies 

were achieved moving the program from research to operational status. In 2007, the CDL became 

operational providing acreage estimates for thirteen states and nine crops to the NASS Agricultural 

Statistics Board for the October Crop Report. For the first time, remote sensing estimates were 

used in consideration for setting the NASS official state acreage estimates, a milestone for the 
11

program. An additional eight CDL state level image products were generated in the post-season 

for a total of twenty-one 2007 CDL products. 

To continue to enhance the scope and timing of the program, research was conducted to 

determine whether accurate estimates could be provided earlier in the season (Boryan et al., 2008). 

Acreage estimates provided to the NASS Agricultural Statistics Board to meet the June Acreage, 

August, September, and October Crop Production Reports, as well as, the September Small Grains 

Report would further NASS’s remote sensing mission. 

In 2009, the CDL program expanded to provide estimates to the Agricultural Statistics 

Board meeting all production deadlines for twenty-seven states and fifteen crops. Twenty 

additional CDL image products were generated after the growing season for a total of forty-eight 

2009 statewide CDL products. For the first time, CDL image products were created for all 

conterminous states in the U. S. 

10. ACCURACY

The accuracy of the CDL agricultural crop categories were calculated by comparing the 

CDL image products with independent validation files from the FSA CLU data. During the ground 

reference preparation phase, 30% of the available FSA CLU polygons data were set aside for use 

in accuracy assessment. The producer and user accuracies were generally 85% to 95% correct for 

the major crop-specific land cover categories. The producer’s accuracy relates to the probability 

that a FSA CLU ground reference pixel was correctly mapped and measures errors of omission. 

The user’s accuracy indicates the probability that a pixel from the classification actually matches

the FSA ground reference data and measures the errors of omission (Congalton and Green, 1999). 

Accuracies for the non-agricultural categories were not provided as these categories were not based 

on known ground reference data, but on a separate land cover classification, the 2001 NLCD, with 

its own inherent rates of error.

11. CROPLAND DATA LAYER IMAGE PRODUCT DELIVERY

In May, 2010, the CDL image products were downloadable free of charge from the 

National Resource Conservation Service (NRCS) Geospatial Data Gateway at 

https://datagateway.nrcs.usda.gov. The CDL image products were raster, georeferenced, 

categorized land cover data layers with a spatial resolution of 30 meters (CDLs produced prior to 

2006 using TM data) and 56 meters (CDLs produced from 2006 - 2009 using AWiFS data). The 

CDL products on the Geospatial Data Gateway were provided in GeoTIFF format, Universal 

Transverse Mercator (UTM), North American Datum (NAD) 1983 or World Geodetic System 

1984 map projection. The CDL products were available on CD as GeoTiff (.tiff) or ERDAS 

imagine (.img) in two map projections including UTM and Albers, NAD 1983, Geodetic 

Reference System 1980. The CDLs were aggregated to standardized categories emphasizing 

agricultural land cover. The associated metadata for each CDL product were included with the 

Geospatial Data Gateway download.
12

12. CONCLUSION

This paper describes how the USDA NASS Cropland Data Layer program transitioned 

from a research to operational program by means of the production efficiencies achieved using the 

FSA CLU data, AWiFS Imagery, See5, ArcGIS and SAS software. In 2009, the NASS Cropland 

Data Layer program provided operational in-season acreage estimates to the NASS Agricultural 

Statistics Board and Field Offices for twenty-eight states and fifteen crops. The CDL program 

covered all NASS speculative program crops providing updated acreage estimates throughout the 

growing season using the most up–to-date farmer-reported and satellite data available. 

Additionally, for the first time in 2009, the freely available CDL products were created for the 

forty-eight conterminous states in the U.S. 

Having achieved this level of coverage in 2009, it is the goal of the CDL program to 

continually provide yearly updates at the state level for research and operational applications, 

including climate change, forest fire assessment, crop rotation and watershed analysis, agricultural 

yield, assessments of urban expansion and much more. Further, the CDL program will continue to 

evaluate its ability to expand the quantity and scope of crop acreage estimates provided to the 

NASS Agricultural Statistics Board and Field Offices to further the NASS mission of providing 

the most timely, accurate and useful agricultural statistics possible.
13

13. REFERENCES

Boryan, C., and M. Craig (2005). Multiresolution Landsat TM and AWiFS Sensor Assessment for Crop Area Estimation in 

Nebraska, Proceedings from Pecora 16, Sioux Falls, South Dakota.

Boryan, C., M. Craig, M. Lindsey (2008). Deriving Essential Dates of AWiFS and MODIS for the Identification of Corn 

and Soybean Fields in the U.S. Heartland, Proceedings from Pecora 17, Denver Colorado, November 2008.

Chang, J. C., M. C. Hansen, K. Pittman, M. Carroll and C. DiMiceli (2007). “Corn and Soybean Mapping in the United

States Using MODIS Time-Series Data Sets,” Agronomy Journal, 99: 2007, 1654-1664.

Congalton, R. G., and K. Green (1999). Assessing the accuracy of remotely sensed data: principles and practices. Boca 

Raton: Lewis Publishers.

Craig, M. (2001). The NASS Cropland Data Layer Program. Presented at the Third International Conference on Geospatial 

Information in Agriculture and Forestry, Denver, Colorado, 5-7 November 2001.

Craig, M. (2005). "Using FSA Administrative Data in the NASS Cropland Data Layer" Draft as of 9/7/2005; write-up of 

FSA data used for Nebraska 2002-2004 research; circulated administratively only in NASS; NASS/RDD/GIB/SARS 

Fairfax, VA 2005.

Craig, M. (2009). A Brief History of the Cropland Data Layer at NASS

https://www.nass.usda.gov/Research_and_Science/Cropland/CDL_History_MEC.pdf Accessed May 10, 2010 

Day, C.D. (2002) "A Compilation of PEDITOR Estimation Formulas". RDD Research Paper RDD-02-03, USDA, NASS, 

Washington, D.C. January, 2002.

Friedl, M., and C. Brodley (1997). Decision Tree of Land Cover from Remotely Sensed Data. Remote Sensing of the 

Environment, 61: 339-409.

Gelder, B., R. Cruse, and A Kaleita (2008). “Automated determination of management units for precision conservation,” 

Journal of Soil and Water Conservation, September 2008 vol. 63 no. 5 pp. 273-279.

Hansen, M., R. Dubayah, & R. Defried. (1996) Classification Trees: An Alternative to Traditional Landcover Classifiers. 

International Journal of Remote Sensing, 17, 1075-1081.

Heald, J (2002). “USDA establishes a Common Land Unit, “ESRI ArcUser Online. April-June 2002, 

<http://www.esri.com/news/arcuser/0402/usda.html > Accessed, February 10, 2010.

Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan, (2004). “Development of a 2001, National Land Cover Database 

for the United States”, Photogrammetric Engineering & Remote Sensing. Vol. 70, No. 7, pp 829-840, July 2004.

Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N. Herold, A. McKerrow, J. N. Van Driel, and J. Wickham 

(2007). “Completion of the 2001 National Land Cover Database for the Conterminous United States”, Photogrammetric

Engineering & Remote Sensing. Vol. 73, No. 4, pp. 337-341, April 2007.

Johnson, D. M. (2008). A comparison of coincident Landsat-5 TM and Resourcesat-1 AWiFS imagery for classifying 

croplands, Photogrammetric Engineering and Remote Sensing. Vol 74, No 11 November 2008.

Lawrence, R., A. Bunn., S. Powell., and M. Zambon (2004). Classification of Remotely Sensed Imagery using Stochastic 

Gradient Boosting as a Refinement of Classification Tree Analysis, Remote Sensing of the Environment, 90.331-336.

Liknes G., C. Perry, and D. Meneguzzo (2010). “Assessing Tree Cover in Agricultural Landscapes Using High-Resolution 

Aerial Imagery,” The Journal of Terrestrial Observation, Vol. 2 No. 1 (Winter 2010), pp. 38-55.
14

Maxwell, S. K., J. Meliker and P. Goovaerts (2010).” Use of land surface remotely sensed satellite and airborne data for 

environmental exposure assessment in cancer research,” Journal of Exposure Science and Environmental Epidemiology

(2010), 20, 176–185 

Mueller, R., C. Boryan and R. Seffrin (2009). Data Partnership Synergy: The Cropland Data Layer. August ’09 GMU 

geoinformatics conference < https://www.infona.pl/resource/bwmeta1.element.ieee-art-000005293489/tab/summary> 

NASS, 2006. Assessment of TM and AWiFS imagery for cropland classification: three case studies.

https://ipad.fas.usda.gov/pdfs/2006/NASS_AWiFS_TM_CDL_comparison_Final.pdf Accessed, February 10, 2010.

Quinlan, J. (1996). Bagging, Boosting, and C4.5, Proceedings of the Thirteenth National Conference on Artificial 

Intelligence, Portland , Oregon (American Association for Artificial Intelligence Press, Menlo Park, California), pp. 

725 – 730.

Seffrin, R. (2007). Evaluating the Accuracy of 2005 Multitemporal TM and AWiFS Imagery for Cropland Classification of 

Nebraska, Proceedings of the ASPRS 2007 Annual Conference, Tampa Florida, May 7-11, 2007.

Shan J., E. Hussain, K. Kim, and L. Biehl (2010). “Flood Mapping with Satellite Images and its Web Service,” 

Photogrammetric Engineering & Remote Sensing, February 2010.

Sun, W., S. Liang, G. Xu, H. Fang and R. Dickinson (2008). “Mapping plant functional types from MODIS data using 

multisource evidential reasoning,” Remote Sensing of Environment, Volume 112, Issue 3, 18 March 2008, Pages 1010-

1024.

USGS (2010). <https://landsat.usgs.gov/products_data_at_no_charge.php>Accessed May 10, 2010.

USGS Newsroom, (2005). “Orthorectified Landsat Digital Data Now Available from the USGS”

<https://www.usgs.gov/news/orthorectified-landsat-digital-data-now-available-usgs >