Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-6703-2025
© Author(s) 2025. 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-17-6703-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Dec 2025
Data description paper |
| 03 Dec 2025
| 03 Dec 2025
CropLayer: a 2 m resolution cropland map of China for 2020 from Mapbox and Google satellite imagery
Hao Jiang
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Mengjun Ku
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Xia Zhou
CORRESPONDING AUTHOR
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Qiong Zheng
Department of Geomatics Engineering, School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
Yangxiaoyue Liu
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Jianhui Xu
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Dan Li
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Chongyang Wang
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Jiayi Wei
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Jing Zhang
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Shuisen Chen
Guangdong Engineering Technology Research Center of Remote Sensing Big Data Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Jianxi Huang
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
College of Land Science and Technology, China Agricultural University, Beijing, China
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Short summary
Existing cropland datasets in China show significant discrepancies. We created a high-resolution cropland map of China for 2020, using imagery from Mapbox and Google. By combining image quality assessments, active learning for semantic segmentation, and results integration. The accuracy achieved to 88.73 %, with 30 out of 32 provincial units reporting area estimates within ±10 % of official statistics. In contrast, only 9 to 1 provinces from 7 existing datasets meet the same accuracy standard.
Existing cropland datasets in China show significant discrepancies. We created a high-resolution...
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