A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data
Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yiel...
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/19/3613 |
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| author | Dailiang Peng Enhui Cheng Xuxiang Feng Jinkang Hu Zihang Lou Hongchi Zhang Bin Zhao Yulong Lv Hao Peng Bing Zhang |
| author_facet | Dailiang Peng Enhui Cheng Xuxiang Feng Jinkang Hu Zihang Lou Hongchi Zhang Bin Zhao Yulong Lv Hao Peng Bing Zhang |
| author_sort | Dailiang Peng |
| collection | DOAJ |
| description | Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks. |
| format | Article |
| id | doaj-art-d6413b4bc336432fa6d59fa9156250a3 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d6413b4bc336432fa6d59fa9156250a32025-08-20T01:47:33ZengMDPI AGRemote Sensing2072-42922024-09-011619361310.3390/rs16193613A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing DataDailiang Peng0Enhui Cheng1Xuxiang Feng2Jinkang Hu3Zihang Lou4Hongchi Zhang5Bin Zhao6Yulong Lv7Hao Peng8Bing Zhang9Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaZhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271002, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAccurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.https://www.mdpi.com/2072-4292/16/19/3613weather forecast datawheat yield predictiondeep-learningtime series |
| spellingShingle | Dailiang Peng Enhui Cheng Xuxiang Feng Jinkang Hu Zihang Lou Hongchi Zhang Bin Zhao Yulong Lv Hao Peng Bing Zhang A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data Remote Sensing weather forecast data wheat yield prediction deep-learning time series |
| title | A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data |
| title_full | A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data |
| title_fullStr | A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data |
| title_full_unstemmed | A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data |
| title_short | A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data |
| title_sort | deep learning network for wheat yield prediction combining weather forecasts and remote sensing data |
| topic | weather forecast data wheat yield prediction deep-learning time series |
| url | https://www.mdpi.com/2072-4292/16/19/3613 |
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