“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.”

FedCASIC Workshops

Washington, DC

Maximizing ArcGIS Pro 

for The Crop Sequence Boundaries (CSB) Project

1

Kevin A. Hunt1, Jonathon Abernethy1, Peter Beeson2, Maria Bowman3, Steven 

Wallander3, and Ryan Williams3

1 USDA, National Agricultural Statistics Service (NASS) 

2 Global Conservation Institute (GCI) 

3 USDA, Economic Research Service (ERS)

Session: Optimize Crop Zoning and Land Planning Boundaries

Date & Time: Thursday, July 13, 2023, at 10:00 am PST

Room: 28B

1

Esri User Conference

San Diego, CA 

Disclaimer: The findings and conclusions in this report are those of the authors and should 

not be construed to represent any official USDA or U.S. Government determination or policy. 
2

1. What are Crop Sequence Boundaries (CSB)?

2. Motivation for CSB

3. Study area

4. Methodology

5. Results

6. Conclusion

7. CSB public release information

Presentation outline
What are Crop Sequence Boundaries (CSB)?

3

CSB represent field-level boundaries over a set time 

frame in a homogenously cropped area.

1. Automatically delineated fields

2. Homogenously cropped areas over a set time frame

3. Physical boundaries and boundaries between different crop types

4. Coverage is complete for the contiguous US
4

Motivation for CSB

Many automatically delineated field polygons exist

• Almost all are small area pilot studies

Need for a contiguous US product that is derived 

from the NASS Cropland Data Layer (CDL)

• Can be used as a standard foundation for 

crop field level geospatial analysis 

• That is publicly available for download

2013-2020 CSB layer (red outline) in 

McLean County, IL, overlaid onto CDL

CSBs Corn Soybeans
5

• The CSB project began in 2020

• Piloted for Illinois, US and

expanded to the contiguous US

• CSBs are created for all years 

between 2008 to 2022 using 8-year 

time frames (i.e., 2015-2022)

• CSB areas compared to estimated 

corn and soybean planted acres

Study area

Study area
Process

polygon

Basic geospatialprocessing steps 

for creating 

polygons from 

the CDL:

Methodology overview 

Filter/clean

6

Google Earth Engine Google Earth Engine

Esri ArcGIS Pro Esri ArcGIS Pro

6

High performance 

cloud computing
7

Methodology

Resample CDL to 10m

Reclassify CDL to Simple Classes

Re-impose Road / Rail Network

Export Desired Years and Extent

Filter Multiple Times (focal-mode)

CDL Processing

Stack Years into Unique Raster

Split Raster by Sub-Regions

Convert Raster to Polygons

Project to Albers Conical

Remove Non-Cropland (n-n, n-1) 

Eliminate under 1 ha into Neighbor

Export Polygons Over 1 Hectare

Merge to State or contiguous US

Create Unique Identifying Code

Populate Attribute Table

Raster Prep

Package for Distribution

Vector Processing Post

-Processing

In Google Earth Engine In Cloud 

In Cloud In Cloud 

ArcGIS Pro

ArcGIS Pro ArcGIS Pro
Methodology

Combine Raster Raster to Vector Vector Elimination

Contiguous US has about 

19.5 million unique polygons

Tuning methods:

• Using too many years- split fields too much

• Using too few years- does not divide fields enough

• Filtering too much – soften edges 

• Filtering too little – leaves islands

• Many choices for fixing edge noise

8
9

Corn Soybean

Year Published* 

(planted acres)

CSB 

(acres)

Error

(%)

Published* 

(planted acres)

CSB 

(acres)

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%

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%

National results for corn and soybean

PEcrop =

Acrop−Tcrop

Acrop

x 100

PE is the percent error, Acrop is the CSB acres,

and Tcrop is the Quick Stats planted acres

*Official estimates published by USDA-NASS

https://quickstats.nass.usda.gov/
10

State results for corn

R2is 0.998

y = 1.01x+-0.143

R2is 0.995

y = 1.03x+-0.488

R2is 0.989

y = 1.01x+-0.2

R2is 0.997

y = 1.01x+-0.232

R2is 0.997

y = 1.01x+-0.175

R2is 0.996

y = 1.01x+-0.293

R2is 0.996

y = 1.02x+-0.311

2015 2016 2017 2018

2019 2020 2021 2022

Log of Quick Stats Acres

Log of CSB Acres

R2is 0.952

y = 0.888x+1.46
11

State results for soybean

2015 2016 2017 2018

2019 2020 2021 2022

Log of Quick Stats Acres 

Log of CSB Acres

R2is 0.991

y = 0.989x+0.0727

R2is 0.998

y = 0.998x+-0.0464

R2is 0.99

y = 0.98x+0.185

R2is 0.991

y = 0.986x+0.0946

R2is 0.997

y = 1x+-0.226

R2is 0.988

y = 1x+-0.131

R2is 0.989

y = 1.01x+-0.183

R2is 0.992

y = 1.04x+-0.736
12

Conclusions

• A repeatable automated process for building crop field polygons

• Now producing large area products because of advancements in cloud computing

• Prioritizing a uniform spatial and temporal methodology which produces a 

streamlined product but likely at the cost of accuracy 

• Future versions need to account for local variabilityand may have to incorporate 

new considerations and advancements in the research 

• Extremely useful applications including aggregating gridded data, improving 

satellite-based estimates of tillage, cover crops and other practices, predicting 

preseason planted acreage and providing a standard for field-level research
13

CSB public release information

Link - https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/

Released this July!

▪ Interactive map to explore data

▪ GDB datasets are available for download

▪ On GitHub algorithm is available 

Example of CSB interactive map
Contact: Kevin.A.Hunt@usda.gov

Thank you

14