Articles | Volume 18, issue 8
https://doi.org/10.5194/essd-18-6065-2026
© 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-18-6065-2026
© 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 Aug 2026
Data description article |
| 25 Aug 2026
| 25 Aug 2026
NortheastChinaMaizeYield10m: a 10 m resolution maize yield dataset for Northeast China (2019–2024) generated via a mechanistically interpretable, field-label-free framework
Jingbo Hu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Xin Du
CORRESPONDING AUTHOR
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Qiangzi Li
CORRESPONDING AUTHOR
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yuan Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Hongyan Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jiansong Luo
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jingyuan Xu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yachao Zhao
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Zhaoming Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yong Dong
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yunqi Shen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
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Jingyuan Xu, Xin Du, Taifeng Dong, Qiangzi Li, Yuan Zhang, Hongyan Wang, Jing Xiao, Jiashu Zhang, Yunqi Shen, and Yong Dong
Earth Syst. Sci. Data, 18, 2413–2441, https://doi.org/10.5194/essd-18-2413-2026, https://doi.org/10.5194/essd-18-2413-2026, 2026
Short summary
Short summary
This study proposed a 20 m soybean yield dataset in Northeast China (NortheastChinaSoybeanYield20m) from 2019 to 2023 using a hybrid framework coupling crop growth model with deep learning algorithm. Stable results were achieved through the years. The overall accuracy of the dataset was 287.44 and 272.36 kg ha–1 in the root mean squared error for field and regional scale, respectively. The study satisfied the urgent demands for precise control of crop yield information.
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Short summary
We produced a 10 m maize yield dataset for Northeast China covering 2019–2024 by combining process-based crop simulations with deep learning. The framework avoids the need for field yield labels during training while maintaining good accuracy against independent observations. The dataset captures both regional yield patterns and fine-scale field variability, supporting agricultural monitoring and management.
We produced a 10 m maize yield dataset for Northeast China covering 2019–2024 by combining...
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