Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain

Yachao Zhao, Xin Du (), Jingyuan Xu, Qiangzi Li, Yuan Zhang, Hongyan Wang, Sifeng Yan, Shuguang Gong and Haoxuan Hu
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Yachao Zhao: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xin Du: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jingyuan Xu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Qiangzi Li: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yuan Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Hongyan Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Sifeng Yan: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Shuguang Gong: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Haoxuan Hu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Agriculture, 2025, vol. 15, issue 12, 1-17

Abstract: The Huang-Huai-Hai Plain is one of China’s primary winter wheat production regions, making accurate yield estimation critical for agricultural decision-making and national food security. In this study, a yield estimation framework was developed by integrating Sentinel-2 and Landsat-8 satellite data with the WOFOST crop growth model and deep learning techniques. Initially, a multi-scenario sample dataset was constructed using historical meteorological and agronomic data through the WOFOST model. Leaf Area Index (LAI) values were then derived from Landsat-8 and Sentinel-2 imagery, and a GRU (Gated Recurrent Unit) neural network was trained on the simulation samples to establish a relationship between LAI and yield. This trained model was applied to the remote sensing-derived LAI to generate initial yield estimates. To enhance accuracy, the results were further corrected using county-level statistical data, producing a spatially explicit winter wheat yield dataset for the Huang-Huai-Hai Plain from 2014 to 2022. Validation against statistical yearbook data at the county level demonstrated a correlation coefficient (r) of 0.659, a root mean square error (RMSE) of 578.34 kg/ha, and a mean relative error (MRE) of 6.63%. These results indicate that the dataset provides reliable regional-scale yield estimates, offering valuable support for agricultural planning and policy development.

Keywords: yield estimation; remote sensing; crop growth model; winter wheat (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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