Articles | Volume 18, issue 3
https://doi.org/10.5194/essd-18-2227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-2227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
25 Mar 2026
Data description article |
| 25 Mar 2026
| 25 Mar 2026
An accurate 10 m annual crop map product of maize and soybean across the United States
Haijun Li
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Bernard Adusei
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Jeffrey Pickering
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Andre Lima
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Andrew Poulson
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Antoine Baggett
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Peter Potapov
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
World Resources Institute, Washington, DC 20002, United States
Ahmad Khan
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Viviana Zalles
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
World Resources Institute, Washington, DC 20002, United States
Andres Hernandez-Serna
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Samuel M. Jantz
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
National Institute for Modeling Biological Systems, University of Tennessee, Knoxville, TN 37996, United States
Amy H. Pickens
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Carolina Ortiz-Dominguez
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Xinyuan Li
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Theodore Kerr
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Zhen Song
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Svetlana Turubanova
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Eddy Bongwele
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Heritier Koy Kondjo
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Anna Komarova
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
Stephen V. Stehman
Department of Sustainable Resources Management, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, United States
Matthew C. Hansen
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
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Binyuan Xu, Hanqin Tian, Shufen Pan, Xiaoyong Li, Ran Meng, Óscar Melo, Anne McDonald, María de los Ángeles Picone, Xiao-Peng Song, Edson Severnini, Katharine G. Young, and Feng Zhao
Earth Syst. Sci. Data, 17, 6353–6377, https://doi.org/10.5194/essd-17-6353-2025, https://doi.org/10.5194/essd-17-6353-2025, 2025
Short summary
Short summary
This study reconstructed the spatial and temporal patterns of four major crops (soybean, maize, wheat, and rice) in South America from 1950 to 2020 by integrating multiple data sources. The results reveal a significant expansion in cropland, particularly for soybean, leading to a substantial reduction in natural vegetation such as forests and grasslands. The datasets can be used to assess the impacts of cropland expansion on water, carbon, and nitrogen cycles in South America.
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Short summary
We developed annual, 10 m spatial resolution maize and soybean maps over the US from 2019 to 2022. Evaluated by ground data collected over a stratified random sample, our maps achieved > 95 % overall accuracy consistently. Our analysis suggested that mixed pixels could be substantially reduced by the increased spatial resolution from 30 to 10 m. Our maps can support research subjects such as forecasting crop yield, analyzing agricultural-related greenhouse gas emissions, etc.
We developed annual, 10 m spatial resolution maize and soybean maps over the US from 2019 to...
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