Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain
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...
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/12/1257 |
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| author | Yachao Zhao Xin Du Jingyuan Xu Qiangzi Li Yuan Zhang Hongyan Wang Sifeng Yan Shuguang Gong Haoxuan Hu |
| author_facet | Yachao Zhao Xin Du Jingyuan Xu Qiangzi Li Yuan Zhang Hongyan Wang Sifeng Yan Shuguang Gong Haoxuan Hu |
| author_sort | Yachao Zhao |
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| description | 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. |
| format | Article |
| id | doaj-art-6bc513a2dfc644729285fba0ee6cc3e2 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-6bc513a2dfc644729285fba0ee6cc3e22025-08-20T03:24:26ZengMDPI AGAgriculture2077-04722025-06-011512125710.3390/agriculture15121257Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai PlainYachao Zhao0Xin Du1Jingyuan Xu2Qiangzi Li3Yuan Zhang4Hongyan Wang5Sifeng Yan6Shuguang Gong7Haoxuan Hu8Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe 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.https://www.mdpi.com/2077-0472/15/12/1257yield estimationremote sensingcrop growth modelwinter wheat |
| spellingShingle | Yachao Zhao Xin Du Jingyuan Xu Qiangzi Li Yuan Zhang Hongyan Wang Sifeng Yan Shuguang Gong Haoxuan Hu Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain Agriculture yield estimation remote sensing crop growth model winter wheat |
| title | Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain |
| title_full | Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain |
| title_fullStr | Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain |
| title_full_unstemmed | Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain |
| title_short | Integrating WOFOST and Deep Learning for Winter Wheat Yield Estimation in the Huang-Huai-Hai Plain |
| title_sort | integrating wofost and deep learning for winter wheat yield estimation in the huang huai hai plain |
| topic | yield estimation remote sensing crop growth model winter wheat |
| url | https://www.mdpi.com/2077-0472/15/12/1257 |
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