Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3213-2024
© Author(s) 2024. 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-16-3213-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
10 Jul 2024
Data description article |
| 10 Jul 2024
| 10 Jul 2024
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Qinghang Mei
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zhao Zhang
CORRESPONDING AUTHOR
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Jichong Han
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jie Song
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jinwei Dong
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Huaqing Wu
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Fulu Tao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Cited
28 citations as recorded by crossref.
- NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023 J. Xu et al. https://doi.org/10.5194/essd-18-2413-2026
- National 10-m soybean maps for South Africa from 2018 to 2025 X. Huang et al. https://doi.org/10.1038/s41597-026-07295-z
- A novel spectral and phenological composite index for the early automatic mapping of potatoes from Sentinel-2 multi-temporal images L. Wang et al. https://doi.org/10.1016/j.compag.2026.111459
- Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041–2100) T. Wang & J. Dong https://doi.org/10.1038/s41597-026-06688-4
- A source-free unsupervised domain adaptation framework for large-scale, in-season soybean mapping P. Tang et al. https://doi.org/10.1016/j.isprsjprs.2026.06.019
- Satellite-based detection of unique sociocultural communities: the case of Mennonite colonies in Bolivia J. Swenson & D. Runfola https://doi.org/10.1080/2150704X.2026.2616622
- Cropland use intensity, stability, and crop transition dynamics in the Songhua River Basin (2000–2024): Implications for sustainable land use and food security L. Gan et al. https://doi.org/10.1016/j.agsy.2026.104652
- Monitoring of Cropland Abandonment Integrating Machine Learning and Google Earth Engine—Taking Hengyang City as an Example Y. Jiang & Z. Guo https://doi.org/10.3390/land14101984
- YOLOv11-Seg-SSC: Soybean Seedling Segmentation and Spatial Localization from Low-Altitude UAV Imagery Y. Yue & A. Zhao https://doi.org/10.3390/agronomy16050536
- Estimating wastewater emissions and environmental levels of typical organic contaminants based on regionalized modelling R. Qin et al. https://doi.org/10.1016/j.envres.2025.120965
- Occurrence of per- and polyfluoroalkyl substances in wheat, maize, rice, and soybean from chinese major grain producing regions X. Li et al. https://doi.org/10.1016/j.jhazmat.2024.136509
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al. https://doi.org/10.1080/20964471.2025.2578056
- Dynamic maps of plastic-mulched farmlands in Northeast China from 1985 to 2025 B. Niu et al. https://doi.org/10.1038/s41597-026-07400-2
- Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
- Vegetable Fields Mapping in Northeast China Based on Phenological Features J. Hu et al. https://doi.org/10.3390/agronomy15020307
- The influence of estimation window configuration on machine learning-based soybean yield estimation across black soil regions S. Huang et al. https://doi.org/10.1016/j.agrformet.2025.110957
- Optimized Phosphorus Application Under Water Stress Enhances Photosynthesis, Physiological Traits, and Yield in Soybean During Flowering Stage Q. Chen et al. https://doi.org/10.3390/agronomy15020444
- Biophysical regulation mechanisms of land surface temperature driven by the spatiotemporal evolution of cropland Z. Xv & A. Lv https://doi.org/10.1016/j.agrformet.2026.111135
- Major grain crop mapping in Northeast China using sample generation method and ensemble learning X. Hu et al. https://doi.org/10.1016/j.eja.2025.127678
- A high-resolution gridded dataset of water footprints for China's major food crops from 2001 to 2020 E. Hua et al. https://doi.org/10.5194/essd-18-4097-2026
- On the Spectral–Phenological Features for Crop Mapping Under Complex Planting Patterns: A Case Study in Jiangsu Province, China Z. You et al. https://doi.org/10.3390/rs18132244
- TWDTW-Based Maize Mapping Using Optimal Time Series Features of Sentinel-1 and Sentinel-2 Images H. Yan et al. https://doi.org/10.3390/rs17173113
- Uncovering the spatiotemporal evolution and driving mechanisms of soybean planting area in China from 2000 to 2022 W. Liu et al. https://doi.org/10.1016/j.jia.2025.07.021
- An Improved 3D U-Net Hybrid Network for High-Precision Soybean Mapping From Multitemporal Sentinel-2 Imagery J. Li et al. https://doi.org/10.1109/JSTARS.2026.3666227
- Quarterly cropland mapping in sample shortage regions using Sentinel-2 images based on the knowledge-driven OCSVM-RF R. Wang et al. https://doi.org/10.1016/j.atech.2025.101572
- An accurate 10 m annual crop map product of maize and soybean across the United States H. Li et al. https://doi.org/10.5194/essd-18-2227-2026
- Global-PCG-10: a 10 m global map of plastic-covered greenhouses derived from Sentinel-2 in 2020 B. Niu et al. https://doi.org/10.5194/essd-17-5065-2025
- NSII: a novel soybean identification index based on Sentinel-2 imagery for accurate and efficient soybean mapping X. Zhu et al. https://doi.org/10.3389/fpls.2026.1788686
28 citations as recorded by crossref.
- NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023 J. Xu et al. https://doi.org/10.5194/essd-18-2413-2026
- National 10-m soybean maps for South Africa from 2018 to 2025 X. Huang et al. https://doi.org/10.1038/s41597-026-07295-z
- A novel spectral and phenological composite index for the early automatic mapping of potatoes from Sentinel-2 multi-temporal images L. Wang et al. https://doi.org/10.1016/j.compag.2026.111459
- Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041–2100) T. Wang & J. Dong https://doi.org/10.1038/s41597-026-06688-4
- A source-free unsupervised domain adaptation framework for large-scale, in-season soybean mapping P. Tang et al. https://doi.org/10.1016/j.isprsjprs.2026.06.019
- Satellite-based detection of unique sociocultural communities: the case of Mennonite colonies in Bolivia J. Swenson & D. Runfola https://doi.org/10.1080/2150704X.2026.2616622
- Cropland use intensity, stability, and crop transition dynamics in the Songhua River Basin (2000–2024): Implications for sustainable land use and food security L. Gan et al. https://doi.org/10.1016/j.agsy.2026.104652
- Monitoring of Cropland Abandonment Integrating Machine Learning and Google Earth Engine—Taking Hengyang City as an Example Y. Jiang & Z. Guo https://doi.org/10.3390/land14101984
- YOLOv11-Seg-SSC: Soybean Seedling Segmentation and Spatial Localization from Low-Altitude UAV Imagery Y. Yue & A. Zhao https://doi.org/10.3390/agronomy16050536
- Estimating wastewater emissions and environmental levels of typical organic contaminants based on regionalized modelling R. Qin et al. https://doi.org/10.1016/j.envres.2025.120965
- Occurrence of per- and polyfluoroalkyl substances in wheat, maize, rice, and soybean from chinese major grain producing regions X. Li et al. https://doi.org/10.1016/j.jhazmat.2024.136509
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al. https://doi.org/10.1080/20964471.2025.2578056
- Dynamic maps of plastic-mulched farmlands in Northeast China from 1985 to 2025 B. Niu et al. https://doi.org/10.1038/s41597-026-07400-2
- Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
- Vegetable Fields Mapping in Northeast China Based on Phenological Features J. Hu et al. https://doi.org/10.3390/agronomy15020307
- The influence of estimation window configuration on machine learning-based soybean yield estimation across black soil regions S. Huang et al. https://doi.org/10.1016/j.agrformet.2025.110957
- Optimized Phosphorus Application Under Water Stress Enhances Photosynthesis, Physiological Traits, and Yield in Soybean During Flowering Stage Q. Chen et al. https://doi.org/10.3390/agronomy15020444
- Biophysical regulation mechanisms of land surface temperature driven by the spatiotemporal evolution of cropland Z. Xv & A. Lv https://doi.org/10.1016/j.agrformet.2026.111135
- Major grain crop mapping in Northeast China using sample generation method and ensemble learning X. Hu et al. https://doi.org/10.1016/j.eja.2025.127678
- A high-resolution gridded dataset of water footprints for China's major food crops from 2001 to 2020 E. Hua et al. https://doi.org/10.5194/essd-18-4097-2026
- On the Spectral–Phenological Features for Crop Mapping Under Complex Planting Patterns: A Case Study in Jiangsu Province, China Z. You et al. https://doi.org/10.3390/rs18132244
- TWDTW-Based Maize Mapping Using Optimal Time Series Features of Sentinel-1 and Sentinel-2 Images H. Yan et al. https://doi.org/10.3390/rs17173113
- Uncovering the spatiotemporal evolution and driving mechanisms of soybean planting area in China from 2000 to 2022 W. Liu et al. https://doi.org/10.1016/j.jia.2025.07.021
- An Improved 3D U-Net Hybrid Network for High-Precision Soybean Mapping From Multitemporal Sentinel-2 Imagery J. Li et al. https://doi.org/10.1109/JSTARS.2026.3666227
- Quarterly cropland mapping in sample shortage regions using Sentinel-2 images based on the knowledge-driven OCSVM-RF R. Wang et al. https://doi.org/10.1016/j.atech.2025.101572
- An accurate 10 m annual crop map product of maize and soybean across the United States H. Li et al. https://doi.org/10.5194/essd-18-2227-2026
- Global-PCG-10: a 10 m global map of plastic-covered greenhouses derived from Sentinel-2 in 2020 B. Niu et al. https://doi.org/10.5194/essd-17-5065-2025
- NSII: a novel soybean identification index based on Sentinel-2 imagery for accurate and efficient soybean mapping X. Zhu et al. https://doi.org/10.3389/fpls.2026.1788686
Saved (final revised paper)
Latest update: 26 Jul 2026
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
In order to make up for the lack of long-term soybean planting area maps in China, we firstly...
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