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GloCAB cropland field boundary dataset.

Joanne V Hall1, Fernanda Argueta1, Louis Giglio1

  • 1Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College Park, MD 20742, United States of America.

Data in Brief
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a large dataset of 190,832 manually-digitized cropland field boundaries across five countries. These digitized field boundaries are valuable for machine learning analyses and understanding cropland characteristics globally.

Keywords:
Cropland field boundaryGeographical information systemManually-digitized field boundariesRemote sensingSentinel-2

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Accurate cropland field boundary data is crucial for agricultural monitoring and analysis.
  • Existing datasets may lack the scale or detail required for comprehensive global studies.
  • The Global Cropland Area Burned (GloCAB) product initiative generated valuable byproducts for agricultural research.

Purpose of the Study:

  • To create and release a comprehensive dataset of manually-digitized cropland field boundaries.
  • To provide a resource for machine learning-based field size analysis.
  • To enable the derivation of cropland field characteristics across diverse geographies and crop types.

Main Methods:

  • Manual digitization of 190,832 cropland field boundaries.
  • Utilized 20 m Sentinel-2 satellite imagery for digitization.
  • Data collected across 22 regions in Brazil, Ukraine, USA, Canada, and Russia.
  • Covered various predominant crop types and time periods.

Main Results:

  • A dataset of 190,832 manually-digitized cropland field boundaries was generated.
  • The dataset spans 5 countries and includes regions ranging from 74 km² to 38,000 km².
  • Field boundaries were digitized over Sentinel-2 imagery, providing high-resolution spatial information.

Conclusions:

  • The released field boundary dataset serves as a valuable resource for agricultural research.
  • It can be used as training data for machine learning models analyzing field sizes.
  • The dataset facilitates the study of cropland characteristics globally.