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ISSN: 1010-6049 (Print) 1752-0762 (Online) Journal homepage: http://www.tandfonline.com/loi/tgei20

Monitoring US agriculture: the US Department of

Agriculture, National Agricultural Statistics Service,

Cropland Data Layer Program

Claire Boryan , Zhengwei Yang , Rick Mueller & Mike Craig

To cite this article: Claire Boryan , Zhengwei Yang , Rick Mueller & Mike Craig (2011) Monitoring

US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland

Data Layer Program, Geocarto International, 26:5, 341-358, DOI: 10.1080/10106049.2011.562309

To link to this article: https://doi.org/10.1080/10106049.2011.562309

Published online: 14 Apr 2011.

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Monitoring US agriculture: the US Department of Agriculture, National

Agricultural Statistics Service, Cropland Data Layer Program

Claire Boryan*, Zhengwei Yang, Rick Mueller and Mike Craig

Department of Agriculture, National Agricultural Statistics Service,

3251 Old Lee Highway, Room 305, Fairfax VA 22030, USA

(Received 2 November 2010; final version received 8 February 2011)

The National Agricultural Statistics Service (NASS) of the US Department of

Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is

a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program

inputs include medium resolution satellite imagery, USDA collected ground truth

and other ancillary data, such as the National Land Cover Data set. A decision

tree-supervised classification method is used to generate the freely available statelevel crop cover classifications and provide crop acreage estimates based upon the

CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural

Statistics Board. This paper provides an overview of the NASS CDL program. It

describes various input data, processing procedures, classification and validation,

accuracy assessment, CDL product specifications, dissemination venues and the

crop acreage estimation methodology. In general, total crop mapping accuracies

for the 2009 CDLs ranged from 85% to 95% for the major crop categories.

Keywords: cropland classification; agriculture; Advanced Wide Field Sensor; crop

estimates

1. Introduction

The mission of the US Department of Agriculture (USDA) National Agricultural

Statistics Service (NASS) is to provide timely, accurate and useful statistics in service

to US agriculture. In 2009, the NASS Cropland Data Layer (CDL) program played

an important role toward fulfilling this mission by providing operational in-season

acreage estimates to the NASS Agricultural Statistics Board (ASB) and Field Offices

(FOs) for 15 crops in 27 states. The 2009 CDL program covered many different

crops, such as corn, soybeans, wheat, rice and cotton, etc. It provided updated

acreage estimates throughout the growing season as increased quantities of farmer

reported and satellite data became available. Revised CDLs, for several key states,

were generated and estimates provided to the ASB and FOs up to six times during

the growing season to provide input in setting acreage estimate updates.

The CDL product is a comprehensive, raster-formatted, geo-referenced, cropspecific land cover classification with a spatial resolution of 56 m that utilizes orthorectified imagery to accurately and geospatially identify field crop types. On 4

January 2010, 48 state-level CDL land cover products, for crop year 2009, were

*Corresponding author. Email: claire_boryan@nass.usda.gov

Geocarto International

Vol. 26, No. 5, August 2011, 341–358

ISSN 1010-6049 print/ISSN 1752-0762 online

This work was authored as part of the Contributors’ official duties as Employees of the United States Government. In

accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.

DOI: 10.1080/10106049.2011.562309

http://www.informaworld.com
publicly disseminated as Geographic Information System (GIS) data layers. Twentyseven state-level CDL products were completed in season and 21 were completed in

the post season. These GIS products are valuable resources for government agencies,

private sector organizations, scientists, educators, and students who use land cover

information.

CDL products have been used in a variety of research applications including

assessing the utility of 500 m Moderate Resolution Imaging Spectroradiometer

(MODIS) Time-Series Data for mapping corn and soybeans in the US (Chang et al.

2007), validating plant functional type maps developed from MODIS data using

multisource evidential reasoning (Sun et al. 2008), examining the relationship

between agricultural chemical exposure and cancer (Maxwell et al. 2010) to flood

mapping assessment with satellite images (Shan et al. 2010). The CDL was also used

to evaluate the use of high spatial 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). Additional reported uses of the CDL products include

agribusiness, change detection, yield, crop intensity and rotation, education, ethanol,

epidemiology, as well as assessments of water use, watershed, environmental risk,

disaster response and forest fire potential.

This paper provides an overview of the current NASS CDL program, including

method and inputs used in CDL production. Additionally, the description of CDL

applications is provided to help users more wisely interpret and take advantage of

the freely available crop-specific land cover classifications for alternative applications. The major inputs to the recent CDL program are detailed including satellite

and ancillary data, sources of ground truth, software, classification and estimation

procedures, accuracy assessment, results, and metadata. Figure 1 illustrates the 2009

state-level CDL image products. The legend identifies aggregated agricultural and

non-agricultural land cover categories by decreasing acreage.

2. Background

The image processing and acreage estimation software first used to create the CDL

was known as Peditor. This ‘in-house’ software, based on Pascal and FORTRAN,

was originally written in the 1970s and was updated and maintained by NASS

through 2006. It included digitizing, labelling, clustering, data pre-processing, Maximum Likelihood classifier, and acreage estimation components. Advantages of

Peditor included the ability to produce statewide CDL image products and accuracy

assessments, link multiple programmes, and most importantly estimate crop acreage

with a simple linear regression method. The quality of the CDL products was high

with classification accuracies ranging in the low to mid-90% for major crops. At the

time, no commercial software could conduct all of the necessary operations performed by Peditor (Ozga and Craig 1995). Additionally, in the early 1990s,

the Remote Sensing Project software was developed using Microsoft Visual FoxPro

to manage the ground truth data collection, digitization and field acreage

correction efforts.

From 1997 to 2005, the NASS CDL program used ground truth collected during

the June Agricultural Survey (JAS). Every June, approximately 11,000 one-square

mile segments are surveyed as a part of the JAS. The JAS segments are made up of

approximately 41,000 individual farms that are enumerated to identify the planting

342 C. Boryan et al.
intentions for all agricultural land within the segments, including planted acreage

and acreage intended for harvest. The selection of JAS segments is based on a

national area sampling frame (ASF) that is the statistical foundation for providing

estimates with complete coverage of US agriculture. The ASF is a stratification of

land cover in the US by percent cultivated cropland.

During this period, the JAS crop data were used as ground truth for maximum

likelihood-based supervised classification. JAS segments were also utilized to

perform a simple linear regression to derive crop-specific acreage estimates (Allen

and Hanuschak 1988, Ozga and Craig 1995). One drawback of the JAS segment data

was that the segments required manual digitization of all field-level boundaries prior

to use in the CDL program, a labour intensive activity. By 2007, the JAS segment

data were no longer utilized within the CDL program as ground truth but were still

used as an independent data source for the regression estimator.

The NASS used multi-spectral satellite imagery beginning in the 1970s to

estimate acreage of large area crops in major producing states. NASS remote sensing

programs initially used imagery from the Landsat Multi-Spectral Scanner instruments through the 1987 crop season at which time NASS began evaluating Landsat 5

Thematic Mapper (TM) and SPOT Image data as possible replacements. In 1991,

Landsat TM was adopted for use in the program. By April 1999, Landsat TM and

Figure 1. The 2009 cropland data layer products. The legend identifies aggregated

agricultural and non-agricultural land cover categories by decreasing acreage.

Geocarto International 343
Landsat Enhanced Thematic Mapper (ETMþ) data were used in combination to

produce crop acreage estimates and CDL image products for six major crop

producing states (Mueller 2000, Craig 2001). On 31 May 2003, the Landsat ETMþ

sensor experienced an anomaly in its scan line corrector at which time NASS began

to evaluate alternative sources of data including imagery from the Indian Remote

Sensing Satellite (IRS) RESOURCESAT-1 launched in October 2003. The IRS

RESOURCESAT-1 Advanced Wide Field Sensor (AWiFS) became the sensor of

choice for the NASS CDL program after careful, quantitative evaluation and

comparison of AWiFS with Landsat data for CDL production (Boryan and Craig

2005, Seffrin 2007, Johnson 2008).

The growth of the CDL program to include more states from 1997 to 2006 was

primarily through partnerships and cooperative agreements with federal and state

governments and universities. It was determined, however, that producing CDLs

within NASS headquarters was the most efficient means to expand the program.

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 remote sensing software, Environmental Systems

Research Institute’s (ESRI) ArcGIS, Statistical Analysis Software (SAS) and new

data sources including RESOURCESAT-1 AWiFS data, and 578 Administrative

and Common Land Unit (CLU) data from the Farm Service Agency (FSA).

Tremendous efficiency gains were achieved due to the modernization allowing for the

generation of in-season crop acreage estimates, a goal never achieved using the older

operational process, methods and data.

In 2007, the CDL program provided acreage estimates for 13 states and nine

crops to the NASS ASB for the October Crop Production Report (PR). For the first

time, remote sensing estimates were used in season for setting the NASS official state

acreage estimates, a milestone for the program. An additional eight CDL state image

products were generated after the growing season for a total of 21 2007 CDL state

products. In 2008, research was conducted by Boryan et al. (2008) to determine if

accurate estimates could be derived earlier in the growing season. A total of 35 2008

CDL state products were generated and, based upon the previous research, acreage

estimates were provided to the NASS ASB for the first time to meet June, August,

September and October production deadlines.

3. Cropland data layer program inputs

The major inputs to the current CDL program include AWiFS, Landsat TM and

ETMþ, MODIS satellite data, the FSA CLU data for agricultural ground truth and

the National Land Cover Data set (NLCD) 2001 for non-agricultural ground truth

and ancillary data sources including US Geological Survey (USGS) digital elevation,

NLCD 2001 tree canopy and NLCD 2001 imperviousness data layers.

3.1. Imagery

The primary source of satellite data used by the CDL program is acquired by the

IRS RESOURCESAT-1 sensor launched in 2003. The payload of RESOURCESAT-1 includes three sensors: the Linear Imaging Self Scanner (LISS) IV, LISS-III

and AWiFS that is the primary sensor for the CDL program. AWiFS specifications

344 C. Boryan et al.
include a 56-m spatial resolution at nadir, a large swath width (740 km), four

channels including green, red, near-infrared (NIR) and middle-infrared (MIR), a

rapid revisit (5-day repeat) capability, 10-bit quantization and a 5-year design life.

The AWiFS has a moderate spatial resolution that is appropriate for identifying

large homogenous crop fields. The large swath is made possible with identical

AWiFS multispectral cameras (A and B) acquiring data with an 8.4 km overlap and

is particularly useful as large geographic areas can be acquired in single day passes.

The spectral characteristics of AWiFS correspond closely with Landsat TM, which is

no coincidence as AWiFS designers matched bands closely to bands two through five

of Landsat TM. Table 1 lists the sensor specifications of Landsat TM vs. AWiFS.

Landsat TM bands two through five are particularly useful for vegetation

assessments specifically, band 2: 0.52–0.60 mm (green) to the green reflectance of

healthy vegetation, band 3: 0.63–0.69 mm (red) for vegetative discrimination, band 4:

0.76–0.90 mm (NIR) to the percentage of vegetative biomass present and band 5:

1.55–1.75 mm (MIR) to the water content of plants (Jensen 2007).

As a member of the USDA’s Satellite Image Archive (SIA) administered by the

Foreign Agricultural Service, NASS has the opportunity to utilize any and all

available AWiFS data collected by the SIA for CDL processing. The AWiFS data

are collected by cameras A & B mounted side by side and acquisitions are identified

by path/row/quad. Camera A (western side of path) acquires data in quads A and C

and camera B (eastern side of path) acquires data in quads B and D. Figure 2

illustrates an AWiFS single date acquisition with quad collections superimposed on

the image.

The majority of AWiFS acquisitions purchased by the SIA cover the Midwestern

and Great Plains states where most of the corn, soybeans and winter wheat are

grown in the US. The data are ortho-rectified and GeoTIFF formatted. They have

10 bit quantization and Lambert Conformal Conic projection. The NASS reprojects

the data to Albers Conical Equal Area (Albers), GRS 1980 (spheroid) and NAD83

(datum), and mosaics same day acquisitions.

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

corrected) Landsat TM and ETMþ data for CDL production as the entire USGS

Landsat Data Archive became available at no charge (USGS 2010). The Landsat

data were downloaded from Glovis (http://glovis.usgs.gov). Image data processing

steps included converting the data from GeoTIFF to ERDAS Imagine image (.img)

format, reprojecting from Universal Transverse Mercator (UTM) to Albers,

Table 1. Landsat Thematic Mapper and Advanced Wide Field Sensor specifications.

TM AWiFS

Altitude 705 km 817 km

Equatorial crossing time 9:45 + 15 min 10:30 + 5 min

Temporal resolution 16 days 5 days

Spatial resolution 30 6 30 m (reflective),

120 6 120 m (thermal)

56 6 56 m

Radiometric resolution 8 bit (256) 10 bit (1024)

Spectral resolution 6 (B, G, R, NIR, SWIR,

MIR) þ Thermal IR

4 (G, R, NIR,SWIR)

Swath width 185 km 740 km

Scene size 184 6 170 km 370 6 370 km

Geocarto International 345
resampling from 30 to 56 m using bilinear interpolation and mosaicing same day

acquisitions. The bilinear interpolation method was selected to more closely

represent the spectral values of the original neighbouring pixels.

National Aeronautics and Space Administration MODIS 16-day Normalized

Difference Vegetation Index (NDVI) composites were also used to supplement the

AWiFS and Landsat TM and ETMþ data. The 250 m MODIS data were

downloaded from the USGS’s Land Processes Distributed Active Archive Center,

resampled to 56 m and reprojected to Albers.

To produce the 2009 CDLs of all 48 conterminous states 477 AWiFS scenes, 1357

Landsat TM scenes, 138 Landsat ETMþ scenes, and 26 MODIS 16 day NDVI

composite images were utilized. AWiFS and Landsat TM and ETMþ data were

selected based on a low percentage of cloud cover and with the goal of matching the

dates of available imagery with the phenological cycle of the crops. Crop progress

and condition information for major crops in all 48 states was utilized by analysts to

determine optimal dates for imagery selection. Crop progress and condition charts

are available on the NASS web site at http://www.nass.usda.gov/Charts_and_Maps/

Crop_Progress_&_Condition/2009/index.asp.

3.2. Ground truth

The main source of agricultural ground truth for the CDL supervised classification

training is the USDA’s FSA CLU data. This standardized GIS data layer of the

nation’s farms and fields was established to support farm commodity, conservation

programs and disaster response (Heard 2002, Anderson et al. 2005). CLU data are

updated every growing season when producers report crop type and crop acreage for

Figure 2. Indian remote sensing satellite resources at 1 – Advanced Wide Field Sensor

imagery acquired on 2 August 2009. Acquisition descriptions include path/row/quad

information. The brightly coloured quads are those used in CDL processing.

346 C. Boryan et al.
their fields to FSA county offices. The FSA CLU program is operational in over

2300 FSA county offices. The program includes all states and extensive coverage of

‘major crops’, which are those for which farmers receive financial subsidies. The

CLU system creates digitized polygon boundaries of semi-permanent ‘fields’ in ESRI

shape file format. Attribute information is maintained in a separate database format

known as FSA 578 Administrative Data (Heard 2002). Two important advantages of

the FSA CLU data for CDL processing are the sheer volume of agricultural data and

that the CLU polygons are digitized in the FSA county offices thereby creating a

comprehensive agricultural data set that requires no manual digitizing by NASS

staff. The FSA CLU data are confidential data sources and are not provided or

shared with anyone outside of NASS. Figure 3 illustrates FSA CLU ground truth

polygons of a 184 km2 area in Nebraska. The yellow polygons are corn fields, dark

green polygons are soybeans and pale green polygons are pasture/grass.

The preparation of FSA CLU data for use in the CDL production occurs in three

phases, the first involves the delineation of CLU polygons using ESRI arc GIS 9.3

software. Certified CLUs are provided by FSA in shape file format at the county

level. The original CLUs are merged to create a state-level shape file and buffered

inward by 30 or 56 m, depending on the state, so that the centre of the crop fields,

rather than the field boundaries or edges are targeted for sampling. The state shape

files are reprojected from UTM to Albers. At this point, the attributes attached to

the CLU polygons include state, county of administration, county of geography,

CLU polygon acreage, farm, tract, CLU number and an NASS unique identifier that

Figure 3. Farm service agency common land unit ground truth polygons.

Geocarto International 347
is generated from a combination of these attributes. The CLU shape file does not

contain crop-specific information. These steps are performed in sequence using

python scripts and ArcGIS processing tools. This phase in ground truth preparation

requires processing only once per state annually. Once the FSA CLU county

polygon data are merged to the state level and buffered, they are ready for linking

with the FSA 578 attribute data that includes all crop-specific information including

crop type, status and intention codes.

Updating the FSA 578 attribute data provides the opportunity to utilize the most

current ground truth available, as farmers continue to report and/or update their

cropping intentions throughout the growing season. The CLU fields are sorted by

crop type, size and attributes so that when separated into ground truth and

validation data sets, they include the optimal range of crops and acreages. CLU

polygons that are either planted to more than one crop or have acreage discrepancies

of more than 10% between the CLU polygon and the 578 attribute data are excluded

from the final ground truth data set. For example, Nebraska FSA 578 attribute data

accessed on 15 September 2009 included 484,410 CLU polygon records. After

filtering on acreage discrepancies and multiple crop types, 251,016 CLU polygon

records remained in the ground truth data set.

Once the FSA CLU polygons are linked with the 578 data, the state-level CLU

shape files are prepared for use with the See5 decision tree software. An important

requirement of See5 is that all inputs must be in raster format of identical cell size

and projection. The shape files are divided into separate training and validation

fields using a 70% training and 30% validation breakdown and converted into

continuous raster layers. The cell size of all raster layers are set to 56 m, a

predetermined cell size for all inputs to match the AWiFS spatial resolution and the

extent of FSA CLU raster layers are set to match the extent of all other inputs to

the classification.

For all of the advantages of the FSA data, there still exists a shortcoming. Many

CLU polygons include more than one crop type per CLU (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 from the ground truth used in the

classification process. Fortunately, this shortcoming is greatly outweighed by the

sheer volume of crop data available from the FSA CLU program. The CLU data

currently stands as the cornerstone of the CDL program. Being a comprehensive

agricultural data set that requires minimal preparation and can be updated multiple

times during the growing season greatly outweighs the disadvantage. Using the FSA

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

timeliness of available ground truth and thereby increased the scope, efficiency, and

accuracy of the operational CDL program.

The current ground truth data source for acreage estimation is still from the JAS.

The 11,000 area segments selected nationwide for the JAS account for approximately

2.5% of total land area in the US JAS segments range in size from one-tenth of

one-square mile in urban areas to approximately one-square mile in cultivated areas

to as much as 4–8 square miles in open range. This stratification of land facilitates

the identification and higher selection rate for segments in intensively cultivated land

areas that takes place at a rate of approximately 1:125. Segments in less-cultivated

areas are selected at a rate of 1:250 to 1:500. The JAS data are based on a probability

survey and considered statistically robust. The 150–400 square miles of ground truth

collected on average per state during the JAS provides the basis for building the

348 C. Boryan et al.
regression estimation model. The farmer reported data collected in the JAS are only

used internally by NASS and held strictly confidential.

3.3. Ancillary data

Several raster-based data layers from USGS were used as ancillary data sources in

the production of the 2009 CDL products. These include the National Elevation

Data set (NED), the NLCD 2001 tree canopy and the NLCD 2001 imperviousness

products. The NED is 30 m in spatial resolution. The tree canopy and

imperviousness layers are by-products of the 2001 NLCD, a national product

completed in January of 2007 (Homer et al. 2004, 2007). These data sets were merged

to create a US national level product, reprojected to Albers and resampled to 56 m

to match the native AWiFS pixel resolution. These ancillary products facilitated the

separation of agricultural from non-agricultural land cover categories. Figure 4

illustrates the NLCD 2001 data of an area in Nebraska, US. Representations include

grey – urban, green – grassland, dark blue – water and light blue – wetlands.

Approximate image area is 765 km2

.

The NLCD 2001 was the source of non-agricultural ground truth for CDL

processing. Features such as water, urban, barren, forest, shrub/scrub, grassland

herbaceous and wetlands were sampled from the NLCD 2001. Since NASS and the

FSA, do not collect non-agricultural ground truth, the NLCD 2001 was deemed to

be the best available source. Although the NLCD 2001 is a dated product, NASS has

found that by using current imagery, the See5 classifier has correctly identified areas

of urban expansion, agricultural land conversion and forest clearing. The NASS has

not made an attempt to quantify these changes.

Figure 4. The National Land Cover Data set 2001 of Nebraska. Categories sampled from the

National Land Cover Data set 2001 include non-agricultural categories such as urban, water,

wetlands and forest.

Geocarto International 349
4. Classification

Supervised classification of the cropland cover type with raw imagery and ancillary

data is performed using the FSA CLU and NLCD 2001 ground truth sample points

as training for the See5 decision tree classifier. Training samples (pixels) are used by

the classifier to derive the state-level decision trees. State-level samples are collected

from the FSA CLU data to create agricultural training data and from the NLCD

2001 to create non-agricultural training data. The NLCD sampling tool kit provided

by USGS is an ERDAS Imagine plug-in component that interfaces ERDAS Imagine

with See5. The NLCD sampling tool kit was customized by NASS to increase the

number of bands of data (83–1000) that could be used as inputs to the classification

process.

Pre-processed AWiFS, Landsat and ancillary data are loaded into the sampling

tool as ‘independent variables’. In the pre-processing phase, images are selected

based on optimal dates for separation of crop types and with maximum geographic

coverage. FSA and NLCD samples are collected separately. When deriving ground

truth sample points, the FSA CLU data layer (or USGS NLCD 2001 data) is loaded

as the ‘dependent layer’. A number, per cent or all points within the dependent layer

is sampled. A random stratified sampling scheme based upon crop or nonagriculture categories is utilized. Names and data files are outputs of this process.

The names files identify the number of training samples selected, values ignored,

sampling method, output form, the dependent layer including the directory path and

all independent layers listed as individual bands.

In classification, See5’s boosting algorithm is set to 10 trials and global pruning at

25% based on positive results in the literature (Quinlan 1996). Analysis is performed

at the pixel level. Positive attributes of See5 include allowing for an abundance of

satellite imagery to be used in the classification process; the powerful See5 boosting

algorithm that reviews the results multiple times to refine or ‘prune’ the decision tree;

and See5’s tolerance of image noise, such as clouds, haze or even scan gaps in the

Landsat ETMþ imagery. The raw state-level CDL image products and the

corresponding confidence layers are produced without any form of smoothing or

filtering of results, the only exception being the citrus category in the state of Florida.

A description of the CDL confidence layer is included in Section 6.3 of this paper.

5. Accuracy

The accuracies of the CDL agricultural crop categories are derived by comparing the

CDLs with independent validation data extracted from the FSA CLU ground truth

data. During the ground truth preparation phase, 30% of the available FSA data (at

the polygon level) are set aside for the purpose of validating the output product at

the pixel level. In CDL production, the Kappa coefficients were used for measuring

the difference between the actual agreement in the accuracy matrix and the

agreement that would occur by chance (Congalton and Green 1999). The number of

‘correct pixels’ in the accuracy table represents the total number of independent

validation pixels correctly identified and quantifies the abundance of crops within a

state. The producer and user accuracies are generally 85% to 95% correct for major

crop categories. Accuracy statistics are included in the metadata provided with all

CDL image products. Accuracies for the non-agricultural categories are not

provided. Table 2 contains an example of the accuracy statistics generated for the

Nebraska 2009 CDL.

350 C. Boryan et al.
Table 2. The accuracy table for the Nebraska 2009 Cropland Data Layer.

USDA, National Agricultural Statistics Service, 2009 Nebraska Cropland Data Layer

Statewide agricultural accuracy report

Crop-specific covers only *Correct Accuracy Error kOverall Accuracy 1669764 91.65% 8.35% 0.8618Cover Type

Attribute

code

*Correct

pixels

Producer’s

accuracy (%)

Omission

error (%) k

User’s

accuracy (%)

Commission

error (%)

Cond’l

k

Corn 1 1019448 97.12 2.88 0.9335 98.64 1.36 0.9679Sorghum 4 7675 59.87 40.13 0.5966 81.81 18.19 0.81668Soybeans 5 447419 96.43 3.57 0.9523 97.44 2.56 0.9657Sunflowers 6 1237 40.58 59.42 0.4054 84.55 15.45 0.8453Popcorn 13 608 19.31 80.69 0.1928 85.39 14.61 0.8537Barley 21 18 15.93 84.07 0.1593 78.26 21.74 0.7826Durum wheat 22 0 n/a n/a n/a 0.00 100.00 0.0000Spring wheat 23 76 11.33 88.67 0.1132 83.52 16.48 0.8351Winter wheat 24 92688 90.72 9.28 0.9021 96.79 3.21 0.9660Other grains 25 103 13.05 86.95 0.1305 69.13 30.87 0.6911WW/soybeans 26 29 13.00 87.00 0.1300 28.71 71.29 0.2870Rye 27 128 8.82 91.18 0.0881 55.17 44.83 0.5514Oats 28 997 25.94 74.06 0.2588 64.20 35.80 0.6412Millet 29 1574 37.19 62.81 0.3709 53.48 46.52 0.5337Alfalfa 36 30093 72.82 27.18 0.7231 89.09 10.91 0.8884Other hays 37 10778 15.85 84.15 0.1526 86.18 13.82 0.8565Beets 41 1360 86.02 13.98 0.8601 91.09 8.91 0.9108

(continued)

Geocarto International 351
Table 2. (Continued).

USDA, National Agricultural Statistics Service, 2009 Nebraska Cropland Data Layer

Statewide agricultural accuracy report

Crop-specific covers only *Correct Accuracy Error kOverall Accuracy 1669764 91.65% 8.35% 0.8618Cover Type

Attribute

code

*Correct

pixels

Producer’s

accuracy (%)

Omission

error (%) k

User’s

accuracy (%)

Commission

error (%)

Cond’l

k

Potatoes 43 1271 83.02 16.98 0.8301 97.62 2.38 0.9762Other crops 44 0 0.00 100.00 0.0000 n/a n/a n/aMisc. vegetables 47 0 n/a n/a n/a 0.00 100.00 0.0000Watermelon 48 0 n/a n/a n/a 0.00 100.00 0.0000Peas 53 0 0.00 100.00 0.0000 n/a n/a n/aClover/wildflowers 58 0 0.00 100.00 0.0000 n/a n/a n/aIdle/fallow 61 54262 85.96 14.04 0.8550 94.56 5.44 0.9437Grapes 69 0 0.00 100.00 0.0000 n/a n/a n/aChristmas trees 70 0 0.00 100.00 0.0000 n/a n/a n/a

Note: *Correct pixels represents the total number of independent validation pixels correctly identified in the error matrix. The shaded areas highlights the Nebraska 2009Cropland Data Layer overall accuracy and specific accuracies for the three major (most acreage) crops grown in Nebraska in 2009.

352 C. Boryan et al.
6. Cropland data layer products

6.1. Crop acreage estimates

One of the major purposes for producing the CDL is to derive the supplementary

crop acreage estimates for various crops. Intuitively, crop acreage can be derived

from counting pixels of a specific crop type. Pixel counting estimates, however,

consistently underestimate the actual acreage number as compared with NASS

official estimates. Therefore, NASS builds a linear regression model from the CDL

pixel data and segment summary data collected as part of the NASS JAS as follows:

Y 1⁄4 a þ bX ð1Þ

where Y is the estimated acres and X is the independent variable representing CDL

classified acres.

The coefficients a and b are estimated from JAS reported acres and CDL

classified acres using a least square estimation method. This method computes the

best-fitting regression line for the observed data (CDL pixels) by minimizing the sum

of the squares of the vertical deviations from each data point (JAS segment) to the

line. The regression is performed at the segment level for all strata on the JAS

segments and classified pixel data. The reported acres of JAS segments and the pixel

summaries of the geographically corresponding fields on the CDL represent

dependent and independent variables, respectively. This CDL-JAS regression

estimation is preferred as it is able to improve upon the JAS estimate based on

the correlation between the JAS reported acres and the CDL pixel count in each

stratum. The remote sensing based acreage estimate from the CDL-JAS regression

model leads to an independent acreage estimate with a lower error rate (coefficient of

variation) than direct expansion alone or direct pixel counting.

In the modelling process, segments identified as outliers that do not fit the linear

regression relationship are reviewed and removed from consideration if in error. The

correlation coefficient R2 is used to measure the goodness of fitting of the regression

line, i.e. the correlation between the CDL classified pixels and the JAS segment

summary data. Figure 5 illustrates a linear regression performed on corn. Pixels

classified to corn in the CDL (X axis) are regressed against JAS segment data

(Y axis).

The regression scatter plot depicts corn planted in stratum 11 (480% cultivated)

in Nebraska 2009. The X axis reflects acres classified in the CDL product. The Y axis

reflects reported acres in the JAS survey data. The small symbols represent JAS

segments. The black, red and green symbols represent segments considered in the

regression formula and were used to generate the acreage estimates. The blue dots

are outliers as identified in the legend.

Presently, the CDL program provides supplementary acreage estimates to the

NASS ASB and FOs to meet the June (winter wheat), August (corn and soybeans),

September (winter wheat, corn, soybeans, cotton, rice, peanuts and other small

grains), October (corn, soybeans and all other major field crops) and December

(county acreage estimates for major crops) production deadlines. To meet this

requirement, updated CDL products are generated multiple times during the season

to provide acreage estimates with the highest accuracy at each point in the growing

season. In 2009, state-level crop acreage estimates were provided to meet NASS

production deadlines for 15 states for the June PR, 14 states for the August PR,

Geocarto International 353
15 states for the September PR, 15 states for the Small Grain Summary and 27 states

and a total of 15 crops for the final October PR.

6.2. The 2009 cropland data layer image products

In 2009, 27 CDL image products were created during the crop season to provide

state-level acreage estimates to the NASS ASB and state FOs. Using funds provided

by the US Environmental Protection Agency Landscape Ecology Branch, 21

additional CDL state image products were generated in the off season for a total of

48 statewide 2009 CDL products. The final CDL products are generated at the end

of the crop season for the October crop report. CDL products created for earlier

reporting deadlines are not released to the public. The CDL products have a spatial

resolution of 30 m for CDLs produced prior to 2006 and 56 m for CDLs produced

from 2007 to 2009. The CDL products on the Geospatial Data Gateway are

provided in GeoTIFF format, UTM and NAD83 or World Geodetic System 1984

(WGS84) map projection. The 2009 CDLs are aggregated to standardized categories

emphasizing agricultural land cover. The 2009 CDL image products, as well as all

historic CDLS, can be downloaded free of charge from the National Resources

Conservation Service Geospatial Data Gateway at http://datagateway.nrcs.usda.

gov. Table 3 summarizes the historic record of statewide CDLs that are available for

free download.

The NASS, in cooperation with George Mason University/Centre for Spatial

Information Science and Systems, recently released a new interactive visualization

portal called CropScape coincident with the release of the 2010 CDL products.

CropScape serves all CDL data as a web service-based interactive map visualization,

Figure 5. A linear regression performed on corn. Pixels classified to corn in the Cropland

Data Layer (X axis) are regressed against June Agricultural Survey segment data (Y axis).

354 C. Boryan et al.
Table 3. The historic record of state-level cropland data layers (1997–2009) available to the

public.

Year

State 1997–2006 2007 2008 2009

Alabama X X

Arizona X X

Arkansas 1997–2006 X X X

California X X

Colorado X X

Connecticut 2002 X

Delaware 2002 X X

Florida 2004 X

Georgia X X

Idaho 2005 X X

Illinois 1999–2006 X X X

Indiana 2000–2006 X X X

Iowa 2000–2006 X X X

Kansas 2006 X X X

Kentucky X X

Louisiana 2004–2006 X X X

Maine X

Maryland 2002 X X

Massachusetts X

Michigan X X X

Minnesota 2006 X X X

Mississippi 1999–2006 X X X

Missouri 2001–2006 X X X

Montana X X

Nebraska 2001–2006 X X X

Nevada X X

New Hampshire X

New Jersey 2002 X X

New Mexico X X

New York 2002 X X

North Carolina 2002 X X

North Dakota 1997–2006 X X X

Ohio 2006 X X X

Oklahoma 2006 X X X

Oregon X X

Pennsylvania 2002 X X

Rhode Island 2002 X

South Carolina X X

South Dakota 2006 X X X

Tennessee X X

Texas X X

Utah X X

Vermont X

Virginia 2002 X X

Washington 2006 X X

West Virginia 2002 X X

Wisconsin 2003–2006 X X X

Wyoming X X

Geocarto International 355
dissemination and querying system. The CropScape web service provides open

geospatial access and navigation, online mapping, statistical analysis, change

detection, data retrieval and distribution. The CropScape web portal is available

at http://nassgeodata.gmu.edu/CropScape.

6.3. Metadata

Each CDL product has a metadata file associated with it. The metadata includes

the following information: identification, data quality, spatial data organization,

spatial reference, entity and attribute distribution and reference. The associated metadata for each CDL is included with the Geospatial Data Gateway

download and at http://www.nass.usda.gov/research/Cropland/metadata/meta.

htm.

6.4. Classification confidence layer

Supplemental accuracy assessment data, in the form of associated confidence layers,

which are not available through the CropScape web portal or the Geospatial Data

Gateway are available by contacting the authors or HQ_RDD_GIB@nass.usda.

gov. The confidence value is not a measure of accuracy for a given pixel in the

classification but rather a measure of how well the decision to identify a pixel

within a specific category fit within the decision tree rule set. Liu et al. (2004)

provided additional information on the use of confidence layers in land cover

classification.

7. Conclusion

This overview of the NASS CDL program included a brief history followed by a

description of the major inputs to the CDL program including the use of AWiFS;

Landsat TM and ETMþ; MODIS satellite data; the FSA CLU and NLCD 2001 for

ground truth and ancillary data sources. Additionally, descriptions of the software

utilized including ArcGIS 9.3, See5, ERDAS Imagine, NLCD tool kit, and SAS;

classification and estimation procedures, accuracy assessment, results and metadata

were provided.

Recently, 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 all 48 conterminous

states in the US. Having achieved this level of coverage, it is the goal of the CDL

program to continue to provide yearly updates, at the state level, to meet the growing

needs of our agricultural stakeholders.

The CDL program will continue to evaluate its ability to expand the quantity,

scope and quality of crop acreage estimates provided to the NASS ASB and FOs to

further the NASS mission of providing the most timely, accurate and useful

agricultural statistics possible. Research will continue in an attempt to improve the

CDL image products and acreage estimates. Techniques for enhancing the quality of

available ground truth, improving the accuracy of small area but high value crops,

improvements to spatial resolution and cropping intensity and rotational analysis

are being investigated.

356 C. Boryan et al.
Acknowledgements

The authors thank the current NASS team working on the CDL program and the many

analysts who worked on the program over the past 40 years. They extend a special thanks to

Karla Koudelka (NASS) and Lee Ebinger (NASS) for their help in preparing the tables and

graphics and to Dr. Barry Haack (George Mason University) for his valuable suggestions

during the writing of this paper.

References

Allen, J.D. and Hanuschak, G.A., 1988. The remote sensing applications program of the

National Agricultural Statistics Service: 1980–1987. U.S. Department of Agriculture,

NASS Staff Report No. SRB-88-08.

Anderson, T., et al., 2005. USDA service center agencies geospatial data management team data

management for common land unit data. Available from: http://www.itc.nrcs.usda.gov/

scdm/docs/DMP-CLU-DataManagementPlan.pdf [Accessed February 10 2009].

Boryan, C.G. and Craig, M.E., 2005. Multiresolution landsat TM and AWiFS sensor

assessment for crop area estimation in Nebraska. Proceedings from Pecora 16, 22–27

October 2005, Sioux Falls, South Dakota.

Boryan, C.G., Craig, M.E., and Lindsey, M., 2008. Deriving essential dates of AWiFS and

MODIS for the identification of corn and soybean fields in the U.S. heartland. In:

Proceedings from Pecora 17, November 2008, Denver, Colorado.

Chang, J.C., et al., 2007. Corn and soybean mapping in the United States using MODIS timeseries data sets. Agronomy Journal, 99, 1654–1664.

Congalton, R.G. and Green, K., 1999. Assessing the accuracy of remotely sensed data:

principles and practices. Boca Raton: Lewis Publishers.

Craig, M., 2005. Using FSA administrative data in the NASS cropland data layer. Fairfax, VA:

NASS/RDD/GIB/SARS. Draft as of 9/7/2005; write-up of FSA data used for Nebraska

2002-2004 research; circulated administratively only in NASS.

Craig, M.E., 2001. The NASS cropland data layer program. Presented at the Third

International Conference on geospatial information in agriculture and forestry, November

2001, Denver, Colorado.

Gelder, B., Cruse, R.M., and Kaleita, A.L., 2008. Automated determination of management

units for precision conservation. Journal of Soil and Water Conservation, 63 (5), 273–

279.

Heard, J., 2002. USDA establishes a common land unit. ESRI ArcUser Online. Available from:

http://www.esri.com/news/arcuser/0402/usda.html [Accessed February 10 2010].

Homer, C., et al., 2004. Development of a 2001, national land cover database for the United

States. Photogrammetric Engineering & Remote Sensing, 70 (7), 829–840.

Homer, C., et al., 2007. Completion of the 2001 national land cover database for the conterminous United States. Photogrammetric Engineering & Remote Sensing, 73 (4), 337–341.

Jensen, J.R., 2007. Remote sensing of the environment: an earth resource perspective. 2nd ed.

Upper Saddle River, NJ: Prentice-Hall.

Johnson, D.M., 2008. A comparison of coincident landsat-5 TM and resourcesat-1 AWiFS

imagery for classifying croplands. Photogrammetric Engineering & Remote Sensing, 74

(11), 1413–1423.

Liknes, G., Perry, C., and Meneguzzo, D., 2010. Assessing tree cover in agricultural

landscapes using high-resolution aerial imagery. The Journal of Terrestrial Observation, 2

(1), 38–55.

Liu, W., Gopal, S., and Woodcock, C.E., 2004. Uncertainty and confidence in land cover

classification using a hybrid classifier approach. Photogrammetric Engineering & Remote

Sensing, 70 (8), 963–971.

Maxwell, S.K., Meliker, J., and Goovaerts, P., 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, 20, 176–185.

Mueller, R., 2000. Categorized mosaicked imagery from the National Agricultural Statistics

Service Crop Acreage Estimation Program. In: Proceedings of the ASPRS 2000

Conference, ASPRS [Available on the CD], May 2000, Bethesda, MD.

Geocarto International 357
Ozga, M. and Craig, M.E., 1995. PEDITOR – statistical image analysis for agriculture. In:

Presentation at the Washington Statistical Society (WSS) Seminar, USDA/NASS, April

1995, Washington, DC.

Quinlan, J.R., 1996. Bagging, boosting, and C4.5. In: Proceedings AAAI-96 fourteenth

National Conference on Artificial Intelligence, Portland, OR.

Seffrin, R., 2007. Evaluating the accuracy of 2005 multitemporal TM and AWiFS imagery for

cropland classification of Nebraska. In: Proceedings of the ASPRS 2007 Annual

Conference, 7–11 May 2007, Tampa, Florida.

Shan, J., et al., 2010. Flood mapping with satellite images and its web service.

Photogrammetric Engineering & Remote Sensing, 76 (2), 102–104.

Sun, W., et al., 2008. Mapping plant functional types from MODIS data using multisource

evidential reasoning. Remote Sensing of Environment, 112 (3), 1010–1024.

USGS, 2010. Available from: http://landsat.usgs.gov/products_data_at_no_charge.php [Accessed May 10 2010].

358 C. Boryan et al.