Utilizing cloud computing for large vector datasets

To identify and map agricultural fields by their boundaries for the entire conterminous

United States (CONUS) has previously presented computationally large challenges.

Leveraging decades of the remotely sensed Cropland Data Layer (CDL) history by utilizing

cloud computing capabilities has expanded the scale of crop field mapping. The CSB

approach is constructed using only public datasets using an algorithm of geospatial

functions accessing clouds by both Google Earth Engine (GEE) and Amazon Web Services

(AWS) and maximizing ArcGIS Pro application and its functions. These GIS functions can be

run in parallel by sub-dividing CONUS into 2,844 county sized regions based on road

boundaries. These regions are merged after parallel processing utilization is complete.

1. Prepare raster Cropland Data Layer histories by filtering and combining in Google 

Earth Engine utilizing cloud processing

2. Retrieve data from Google Cloud Buckets via gsutil into Amazon Web Services 

storage to be utilized for parallel processing at the sub-region level

3. Run arcpy utilizing ArcGIS Pro in parallel to create CSB

4. Run arcpy utilizing ArcGIS Pro in parallel to prep CSB by adding attributes

Economic Research Service &

National Agricultural Statistics Service

BETA

Corn

Soybean

Spring wheat

Sugarbeets

Dry beans

Barley

Sunflower

Potatoes

Alfalfa

Fallow/idle

Not cropland

Crop type

2021 2020

2019 2018

2017 2016

2015 2014 This map is showing CSB using an 8-year

time frame displaying crop types from

2021 for the conterminous United States

The Crop Sequence Boundaries (CSB) Project

Kevin Hunt1, Peter Beeson

2

, Steven Wallander

3

, Ryan Williams

3

, and Jake Abernethy

1

1 United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) 

2 Global Conservation Institute (GCI)

3 United States Department of Agriculture (USDA), Economic Research Service (ERS)

* Correspondence: kevin.a.hunt@usda.gov

What are Crop Sequence Boundaries (CSB)?

Crop Sequence Boundaries (CSB) are geospatial algorithm-based field polygons. The

growth of gridded datasets, such as the NASS Cropland Data Layer (CDL) (3), has fostered

applications that require field boundaries for aggregating the data to an analysis unit (4).

Most efforts to build algorithm-based field polygons have focused on limited

geographies or on individual years. CSBs are created to provide full coverage for the 48

conterminous United States (CONUS) and to be accurate and repeatable across

years. CSBs use a specified time frame

of historic CDLs together with road

and rail networks to capture the

crop sequence in these synthetic fields.

Learn more about this beta vector-based dataset:

U.S. Crop Sequence Boundaries for 2021 Eight-year Crop Rotations with Boundaries

Impacts of this project

The CSB will be utilized as an algorithm-based geospatially delineated field that can

contain supplemental information and improve methods for area summaries for major

crops. The CSB has utility as a new standard agricultural field proxy that can incorporate

available time frames. The cloud-based methods of development are enhancing large

scale crop mapping with vector-based data. Many applications are being researched

including crop modeling and georeferencing agricultural fields.

Geospatial functions used in CSB development

Esri User Conference

July 11-15, 2022, San Diego, CA

In ArcGIS Pro

Split raster by sub-regions

Reclassify/Filter CDLs 

Convert Raster to Polygons

Project to Albers Conical

Combine years into raster

Eliminate < 1 ha (4 times)

Export over 1 hectare

Spatial joins to geographies

Calculate geometries

Use multiple processors in cloud

Merge to State or CONUS

Package for Distribution

Run zonal statistics

Use ArcGIS or GEE

Acknowledgments

This project has relied on the essential support of many including Maria

Bowman3, Claire Boryan1, Rachael Jennings1, David Johnson1, Eileen

O’Brien1, Warner Rose5, Luca Sartore4, Jessica Spencer5, Patrick Willis1,

and Ying Zhang5

4 National Institute of Statistical Sciences (NISS) 

5 Accenture Federal Services (AFS)

Disclaimer: The findings and conclusions in this presentation are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.

Combine

Create

Distribute

Prepare

Google 

Bucket

Prepare CDL

Virtual 

Machine

Data Data Store

flow

Distribution

1

Retrieve 

data

Creation

with arcpy

AWS* 

Storage

Prep 

with arcpy

4

3

High performance computing cloud

Google Cloud

Google Earth Engine

Esri ArcGIS Pro

2

Processing Processing 

utilizing ArcGIS

*Amazon Web Services

Crop Sequence Boundaries produced by: U. S. Department of Agriculture,

Economic Research Service and National Agricultural Statistics Service

Data Sources: 2014-2021 Cropland Data Layers and U.S. Census TIGER lines [1, 2]

Cartographic Generalization: Smaller categories are combined with larger categories

and colors match other Cropland Data Layer product visualizations

Projection: Albers Equal Area Conic Projection

Map Production: Esri ArcGIS Pro 2.9.2

• Combine Cropland Data 

Layer histories 

• Create synthetic field 

boundaries based on crop 

rotations

• Utilize vector-based 

synthetic fields for analysis

Citations

1. USDA National Agricultural Statistics Service Cropland Data Layer. 2021. Published crop-specific data layer 

[Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed on May 16, 2022; verified on 

February 14, 2022). USDA-NASS, Washington, DC.

2. 2019 TIGER/Line Shapefiles (machine-readable data files) / prepared by the U.S. Census Bureau, 2019 

3. Johnson, D.M.; Mueller, R. The 2009 cropland data layer. Photogramm. Eng. Remote Sens. 2010, 76, 1201–

1205.

4. Beeson, P.C.; Daughtry, C.S.T.; Wallander, S.A. Estimates of Conservation Tillage Practices Using Landsat 

Archive. Remote Sensing. 2020, 12, 2665.

Boundaries

Year 1

Year 2

Year 3

Time Frame

Example 3-year crop sequence boundaries