The findings and conclusions in this report are those of the author(s) and should not be construed to 

represent any official USDA or U.S. Government determination or policy.

Crop Sequence Boundaries (CSB):

Delineated Fields Using Remotely Sensed Crop Rotations

Kevin A. Hunt

USDA-NASS, Washington, DC, USA – Kevin.A.Hunt@usda.gov

Jonathon Abernethy

USDA-NASS, Chicago, IL USA – Jake.Abernethy@usda.gov

Peter Beeson

Global Conservation Institute, Santa Fe, NM, USA – PBeeson@global-conservation.org

Maria Bowman

USDA-ERS, Washington, DC, USA – Maria.Bowman@usda.gov

Steven Wallander

USDA-ERS, Washington, DC, USA – Steve.Wallander@usda.gov

Ryan Williams

USDA-ERS, Washington, DC, USA – Ryan.Williams@usda.gov

Abstract

Gridded landcover datasets like the NASS Cropland Data Layer (CDL) provide a useful resource 

for analyses of cropland management. However, many farm operation decisions are made at the 

field level, not the pixel level. To capture relationships between land cover and field 

characteristics – size, contiguity, etc. – some method is needed to aggregate gridded data into 

crop fields. To provide a uniform and consistent approach for aggregation of gridded data at the 

field level over a series of years, this research project developed a set of Crop Sequence 

Boundaries (CSBs), which are polygons that delineate areas of homogeneous cropping 

sequences. The CSBs are open-sourced algorithm-based, geospatial polygons derived using 

historic CDLs together with road and rail networks to capture areas with common cropping 

sequences. Most efforts to delineate fields using algorithm-based polygons built from remote 

sensing data have focused on limited geographies or individual years. The CSBs were created to 

provide full coverage for the contiguous US and to be accurate and repeatable across years. 

Initial assessments of CSB accuracy find overall high accuracy according to corn and soybean 

acreage. This new expansion of crop field mapping is possible using cloud computing. The CSB 

approach used geospatial functions in Google Earth Engine (GEE) and in the ArcGIS Pro 

application. These geospatial functions are run in parallel, in Amazon Web Services (AWS), by 

sub-dividing the contiguous US into smaller regions based on road and rail boundaries to prevent 

overlaps or gaps in the data. The regions were merged after processing was complete. As a new 

set of algorithmically delineated field polygons, the CSBs enhance applications requiring largescale crop mapping with vector-based data.

Keywords: Statistical analysis; Landcover; Cropland; Geospatial; CDL.
2

1.0 Introduction

The United States Department of Agriculture (USDA) has two of the thirteen federal principle 

statistical agencies: the Economic Research Service (ERS) and the National Agricultural 

Statistics Service (NASS). These two agencies continuously collaborate and coordinate on the 

compilation and analysis of data and the dissemination of information for statistical purposes [1].

One example is the NASS Cropland Data Layer (CDL). The CDL provides operational in-season 

crop type estimates at 30-meter resolution by utilizing multispectral imagery from Earth 

observational satellites and a classification algorithm trained on crop acreage reporting data 

maintained by USDA’s Farm Service Agency (FSA). This gridded dataset is produced for the 

contiguous US and is disseminated annually to the public following the completion of the 

growing season. The CDL for the contiguous US has been available each year since 2008 [2]. 

The new, algorithmically delineated field polygons, called Crop Sequence Boundaries (CSBs), 

presented in this paper were developed in a collaborative effort between ERS and NASS by 

leveraging a time series of historical CDLs. The CSBs, which are scheduled for release summer 

2023, are field-level boundaries for areas with homogenous crop rotation histories. The field 

boundaries are automatically and algorithmically, rather than manually, delineated. Other field 

boundary datasets are often manually delineated, represent legal or administrative boundaries, 

and, most importantly for analysis of gridded datasets, frequently do not necessarily correspond 

to the idea of a field as an area with a uniform crop rotation because legal land units are often 

split or combined into effective management areas, i.e., fields.

Geospatial research has been developing automated crop field delineations methodologies for 

over 30 years. Most studies have been published in the last three to five years, a reflection of 

recent improvements in data, computing power, and methods. Geographically, most studies 

focus on relatively small regions to demonstrate and test methodological advances. Within the 

United States, these studies have focused on multiple states [3], [4], Iowa [5], and Illinois 

[6]. Only one study to date has developed automatic crop-field delineated polygons for the 

contiguous US [7]. Among the other studies, the geographic scope have included study areas in 

China [8], [9], [10]; South America [11], [12]; Europe [13], [14], [15] [16]; Africa [17], [18], 

[19]; Southeast Asia [20], [21]; Turkey [22]; Saudi Arabia [23]; and Australia [24]. Only one 

study has implemented an automatic crop-field delineation methodology globally [25].

Two broad approaches are used in the automatic crop field delineations literature: the zone 

method and the edge intensity method. Most studies primarily rely on only one of these methods, 

although some studies use a mix of the two. The zone method uses contiguity of similar pixels 

(single crop or sequence) and can have multiple crops in a field polygon. An approach similar to 

the zone method was previously proposed for Iowa [5]. The edge intensity method implies one 

crop type or some physical separating boundary [7].

The objective of this project is to produce CSBs for the contiguous US. The CSB algorithm uses

a novel approach to capturing crop fields by employing the zonal method to a stack of historical

CDL years. By combining multiple years of historical CDLs, the zonal method can accurately 

identify homogenously cropped regions while maintaining information on their crop-specific
3

sequences and acreages. To further improve the accuracy of the zonal method, the fields are 

masked with a spatial data layer of US roads and rails [26]. This maintains edges where

homogenously cropped fields border each other with the same crop rotation but physically are 

separated by a road or rail line. The resulting CSBs were validated by comparing corn and 

soybean acreages to the NASS estimated total planted acres, for each contiguous state and 

nationally.

The paper is organized as follows: The study area and data are described in Section 2. The study 

methodology is introduced in Section 3 followed by results and discussion in Section 4. Finally, 

the conclusions are presented in Section 5.

2.0 Study Area and Data

2.1 Study Area - Contiguous US

The contiguous US is the study area (Figure 1), which does not include the non-contiguous states 

of Alaska and Hawaii or US territories. In terms of US agricultural production, corn and 

soybeans represent the two largest crops planted by acreage for this study area.

Fig. 1. The contiguous US, highlighted in black.

2.2 Cropland Data Layer (CDL)

The CDL is a 30-meter pixel-based gridded dataset that represents all landcover across the 

contiguous US. Currently, the CDL represents over 200 types of crops or cropping patterns with 

high levels of accuracy for major commodities, such as corn, soybeans, wheat, rice, and cotton. It 

has been published with complete contiguous US coverage annually since 2008. The two major 

commodities, corn and soybeans, have accuracies above ninety percent [2]. The CDL has been a 

useful dataset for agricultural analysis and statistics.

2.3 Cropland Sequence Boundaries (CSB)

Currently, each of the CSBs are comprised of 8 consecutive years of stacked CDLs. The 

sequence time frame indicates the range of years included in the dataset. For example, the 2015-

2022 CSBs include the CDL years 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022. The 

coverage of the CSBs consist of the contiguous US. The complete set of eight CBS, consisting of 

2008-2015 CSBs to 2015-2022 CSBs, have been completed 2008 to 2022 and are assessed in this 

paper.
4

2.4 NASS Quick Stats Corn and Soybean Agricultural Estimate Data

NASS produces planted acreage estimates for corn and soybeans for most US states; these are 

considered the ground truth for total planted acres of corn and soybeans at the state and national 

levels. The planted acreage estimates are available at the end of the season on Quick Stats [26] 

and are used for validation purposes. 

3.0 Methodology

The approach closely followed the steps described in Beeson et al. 2020, where the first 

generation of the CSBs (referred to as Crop Management Units) were developed.

By design, the CSB polygons are developed from only publicly available datasets: the NASS 

CDL [2] and the US Census TIGER line data [27]. The algorithms are an automated arcpy script 

utilizing typical GIS tools requiring no manual drawing of field boundaries. As fields may divide 

or combine over years, multiple CSB windows were examined. An eight-year window was used

for the first release of the CSBs, but any duration can be used. The initial steps are to filter and 

simplify CDL classes on each historic CDL year to reduce noise and re-impose road and rail 

network line data lost when filtered. These steps are completed in Google Earth Engine (GEE). 

The resulting rasters are then brought into ArcGIS to be stacked and converted into polygons on 

a sub-region basis. Small polygons, under 10,000m2, are eliminated into the next largest polygon 

to remove islands within fields and clean field edges using ArcGIS geospatial tools. Multiple 

processor environments allow for faster completion of this step as sub-region pools can be 

spawned to a core when available. Here, the AWS environment available to the USDA has 96 

cores. The resulting CSBs represent areas of similar cropping sequences for the designated years. 

These steps can be repeated for the 8-year moving window to produce the contiguous US

resulting in at least eight unique layers of CSB history for all lower 48 states (2008-2015, 2009-

2016, 2010-2017, 2011-2018, 2012-2019, 2013-2020, 2014-2021, and 2015-2022).

3.1 CDL Pre-Processing

The primary data input to the CSBs is the NASS CDL [2], which is also the input for numerous

other field delineation algorithms [3], [4], [5], [6], and [7]. In each of these, the CDL undergoes 

some degree of pre-processing two major reasons. First, noise reduction in the CDL is

needed. The noise in the CDL is well documented [28] but has been less in the most recent years

[29], making noise reduction less important but still useful in earlier years. Second, even in areas 

with limited noise, simplification is needed given the focus on cropland and the desire to limit 

the number of unique sequences with stacking multiple years. 

Setting aside some of the important computing steps, four structural changes to the data are made

during pre-processing: resampling, reclassifying, filtering, and masking known edge features 

(roads and railroads).

Resampling: The first change to the raw CDLs is a resampling from 30-meter resolution to 10-

meter resolution. The purpose of this step is to improve the masking (reimplementation) of road 

and rail networks. Because many rural roads and rail routes are less than 30 meters in width, 

imposing them on a 30-meter gridded dataset introduces errors in field edges, particularly where 
5

the transportation features run diagonally across the gridded data. The edge noise, without 

resampling, leads to a downward bias in field areas by assigning too many pixels to roads and 

rail lines.

Reclassifying: The original CDLs have over 200 different classifications for land covers, so 

stacking multiple years of CDLs can lead to hundreds of thousands of different unique 

sequences. The reclassification step aggregates the land cover types into a smaller set of classes.

Filtering. A number of other projects have developed alternative methods for filtering and 

smoothing the CDL [30]. For the CSBs, the filtering process iterates though multiple steps. First 

the CDL is filtered using a focal mode based on patch sizes greater than 40 pixels with a radius 

of four pixels and run with eight iterations. Then the CDL is filtered again (without patch size 

limits) to eliminate speckled CDLs and exaggerate field boundaries. 

Reimposing transportation features: To correct for any misclassification of roads and rails that 

may have been introduced by the filtering process, or that might have been misclassified in the 

original CDL, the CSB process uses publicly available transportation feature classes to impose 

road and rail networks, which are imported from Census Tiger files. The roads are available on 

GEE, but the rail network has to be uploaded as a local asset.

Figure 2. Comparison of 2014 CDL before and after filtering.

3.2 Zonal Identification

Using the modified CDLs for each crop year, the CSBs are developed through six steps: 

subregion definition, stacking, masking, polygon conversion, refinement, and crop code 

repopulation. 

Subregion definition: Given the large processing requirements for the following steps, each

processed CDL is split into subregions. These subregions are defined using the transportation 

network features. This avoids the problem of splitting fields along administrative boundaries 

(e.g., states and/or counties lines) or biophysical boundaries (e.g., watersheds). The size of the 

subregion and the number of subregions allows for efficient multi-core processing. 

Stacking: The current CSBs are developed using a stack of 8 CDL years, but any duration could 

be used. A shorter window, using fewer years, will result in larger polygons that may not divide 
6

fields as much. A longer window, using more years, will result in smaller polygons that possibly 

divide the fields more than expected. If a project spans a 10 year period, it would be appropriate 

to use the same 10-year window CSB to match. If a project studies rotations on a 3-year basis, 

then a CSB window in multiples of three would be appropriate (e.g., 3, 6 or 9 years). The 8-year 

window was chosen as a good compromise for not over or under dividing fields.

Figure 3. Creation of sequence zones by stacking eight filtered CDLs and using combine raster

function, then edge artifact cleaning to final CSBs

Masking: All sequences that do not involve any crop classes or only one year of cropping are 

removed.

Polygon Conversion: Each zone of adjacent pixels with the same sequences are converted into 

polygons. In addition to aggregating (joining) similar pixels, the conversion step simplifies the 

polygons to reduce the number of extra vertices. The polygons are projected to Albers Conical to 

prevent adding artifacts that look like steps in otherwise straight lines while in raster format.

Refinement: The eliminate function is used to avoid downward bias in area, thus preserving the

aggregate total area as much as possible, resulting in a continuous border between CSB 

polygons. The model runs elimination four times. The first time the selected polygons are less 

than 100m2; in the second they were less than 1,000m2; and for the last two, the polygons are less 

than 10,000m2.

 

Repopulating: Repopulating the attribute table uses zonal majority of CDL classes as well as 

county and state locations. A unique ID is added after all sub-regions were joined.

3.3 CSB Acreage Validation

One way of validating the accuracy of the CSBs is to compare the CSB derived acreage to the 

available ground truth planted acreage. Ground truth data are available for a wide range of crops 

on the USDA NASS Quick Stats website. Planted acreages on Quick Stats are available at the 

county, state, and national levels. 
7

To calculate the CSB-based acreage, a crop type is assigned to each CSB based on the majority 

class of CDL pixels within each polygon. For a given county and crop, the CSB-derived, countylevel planted acreage is the sum of the areas of each CSB within the county containing the crop 

of interest (1) 

Acrop = ∑csb∈county Acsb Icsb(crop) (1)

where,

Acrop = the area of the crop in the county, the sum is over CSBs contained in the county, Acsb is

the area of the CSB, and Icsb(crop) is equal to one if the CSB has the crop and zero otherwise. 

The same procedure can be conducted by state and for the contiguous US.

Once the county, state and national estimates are obtained from the CSBs, the error can be 

calculated using the percent error:

PEcrop =

Acrop − Tcrop

Acrop

where PE is the percent error and Tcrop is the ground-truth planted acreage of the crop derived 

from Quick Stats. Since the CSBs represent a sequence of years, each CSB can be summarized 

by a sequence of percent errors, one for each year and crop type. 

4.0 Results and Discussion

Initial 8-year CSB creation for the contiguous US took about five days using a 96-core 

AWS workstation. The process is fully automated, but the sizes of the 86 subregions are not 

balanced. Some sub-processing regions are completed in a couple hours while a few take five 

days. This could be improved to reduce the time to two days. A majority of the processing time 

was spent on the elimination step, which incrementally reduces the number of total polygons. 

This step is run four times by dissolving the selected polygons into the neighbor that shared the 

longest border. The first elimination process started at nearly 500 million polygons and dissolved 

about 100,000 that were less than 100 m2. The second pass eliminated 250 million polygons that 

were less than 1,000m2. The third pass eliminated another 100 million polygons that were less 

than one hectare. The last pass started with over 50 million polygons and reduced the remaining 

by 50 percent. After the elimination step, only polygons greater than one hectare were kept as 

final CSBs which reduced the resulting 25 million polygons to fewer than 20 million. Without 

incrementally increasing the elimination size, the tool often fails or takes days.

The area totals for each crop follow the accuracy of the initial CDL input. In general, compared 

to the Quick Stats national acreage, values produced by the CSB are higher (table 1). This is due 

to the elimination step forcing the polygons to match their neighbors’ edges by expanding into 

non-cropland or edge noise.

Corn Soybean

Year Quick Stats (ac) CSB (ac) Error Quick Stats (ac) CSB (ac) Error

2015 88,019,000 89,888,422 2.1% 82,660,000 87,120,721 5.4%

2016 94,004,000 96,665,222 2.8% 83,453,000 87,644,495 5.0%

2017 90,167,000 93,440,276 3.6% 90,162,000 96,119,359 6.6%

2018 88,871,000 92,904,634 4.5% 89,167,000 95,515,323 7.1%
8

2019 89,745,000 93,459,732 4.1% 76,100,000 80,548,849 5.8%

2020 90,652,000 95,060,605 4.9% 83,354,000 88,401,544 6.1%

2021 93,252,000 97,139,581 4.2% 87,195,000 93,138,351 6.8%

2022 88,579,000 93,071,290 5.1% 87,450,000 94,088,968 7.6%

Table 1. Percent error for CSB’s compared to Quick Stats national planted acreage for corn and 

soybean.

When CSB acreage for corn at the state level is compared to Quick Stats reported planted corn 

acres, the results are generally the same nationally (figure 5). The CSB acreage is higher with a 

slope value above 1.0 with few outliers at the lower values. However, the earliest year, 2015, had 

a lower slope value at 0.888. The largest outlier is Nevada and has under reported values in 2015 

and 2018.

Figure 5. CSB corn planted acreage compared to Quick Stats by state (fit line is dashed and the 

one-to-one line is solid).

For soybean CSB acreage at the state level, the slope value has trended above 1.0 in the last 

several years (figure 6). Generally, soybeans have greater outliers in both higher and lower 

acreage than Quick Stats. Some of the outlier states with lower CSB areas are Texas, Oklahoma, 

and Florida. New Jersey and Delaware are outliers in the positive direction. Soybeans tend to 

have a wider range of results, and the r2value is slightly lower than that for corn.

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9

Figure 6. CSB soybean acreage compared to Quick Stats by state (fit line is dashed and the oneto-one line is solid).

5.0 Conclusions

Creation of the CSBs is a repeatable automated process for building crop field polygons with 

accurate representation of crop areas. Geospatial research into automatic crop-field delineation 

has been studied for many years; however, with advancements in accessibility to cloud 

computing and a growing historical CDL archive, it is now possible to produce the contiguous 

US products demonstrated in this paper using this method. 

The CSBs may benefit from future refinement. The current version represents an early iteration,

prioritizing a uniform spatial and temporal methodology. This produces a streamlined product 

but likely at the cost of accuracy for some geographical areas due to unique factors that vary 

across physical landscapes. Future CSB versions may need to incorporate new considerations 

and advancements that account for local variability.

This novel approach can provide a solid methodology for advancing automatic crop field 

delineation in the US and around the world. The CSBs have been extremely useful including 

predicting preseason planted acreage for corn and soybean [30], and georeferencing farms [31]

and have fully utilized high performance computing cloud resources, including Google Earth 

Engine (GEE) and Amazon Web Services (AWS) in their creation [32].

 

 

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10

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