Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods
Timely and accurate prediction of large-scale crop yields is critical for national food security. Solar-induced chlorophyll fluorescence (SIF), an indicator of photosynthesis, has emerged as a promising predictor of crop yields. However, it remains unclear to what extent satellite-based SIF data can...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2025.2455940 |
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| _version_ | 1832589669105139712 |
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| author | Minxue Zheng Han Hu Yue Niu Qiu Shen Feng Jia Xiaolei Geng |
| author_facet | Minxue Zheng Han Hu Yue Niu Qiu Shen Feng Jia Xiaolei Geng |
| author_sort | Minxue Zheng |
| collection | DOAJ |
| description | Timely and accurate prediction of large-scale crop yields is critical for national food security. Solar-induced chlorophyll fluorescence (SIF), an indicator of photosynthesis, has emerged as a promising predictor of crop yields. However, it remains unclear to what extent satellite-based SIF data can predict crop yields at the regional scale compared to the newly proposed Near-Infrared Reflectance of Vegetation (NIRv). Using multiple statistical machine learning (ML) methods, this study investigated the predictive abilities of SIF and NIRv by combining climate data to predict winter wheat yields in five provinces in the North China Plain (NCP). Results showed that: (a) SIF outperformed NIRv in predicting winter wheat yields. However, in the Extreme Gradient Boosting (XGB) model, SIF’s predictive performance was better than that of the combination of SIF and NIRv, indicating that combining SIF and NIRv could not completely enhance SIF’s predictive performance. (b) Random Forest (RF) and XGB models were significantly better than the other models in yield prediction; specifically, the RF model had high stability. The results highlighted the benefits of combining multiple sources of data and revealed the advantages of RF and XGB models in crop yield prediction in the major grain production region. |
| format | Article |
| id | doaj-art-f197da7ddddf4b90966e81ac32a6456f |
| institution | Kabale University |
| issn | 2279-7254 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-f197da7ddddf4b90966e81ac32a6456f2025-01-24T11:09:39ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2025.2455940Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methodsMinxue Zheng0Han Hu1Yue Niu2Qiu Shen3Feng Jia4Xiaolei Geng5School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaTimely and accurate prediction of large-scale crop yields is critical for national food security. Solar-induced chlorophyll fluorescence (SIF), an indicator of photosynthesis, has emerged as a promising predictor of crop yields. However, it remains unclear to what extent satellite-based SIF data can predict crop yields at the regional scale compared to the newly proposed Near-Infrared Reflectance of Vegetation (NIRv). Using multiple statistical machine learning (ML) methods, this study investigated the predictive abilities of SIF and NIRv by combining climate data to predict winter wheat yields in five provinces in the North China Plain (NCP). Results showed that: (a) SIF outperformed NIRv in predicting winter wheat yields. However, in the Extreme Gradient Boosting (XGB) model, SIF’s predictive performance was better than that of the combination of SIF and NIRv, indicating that combining SIF and NIRv could not completely enhance SIF’s predictive performance. (b) Random Forest (RF) and XGB models were significantly better than the other models in yield prediction; specifically, the RF model had high stability. The results highlighted the benefits of combining multiple sources of data and revealed the advantages of RF and XGB models in crop yield prediction in the major grain production region.https://www.tandfonline.com/doi/10.1080/22797254.2025.2455940Yield predictionmachine learningsolar-induced chlorophyll fluorescencewinter wheatvegetation indices |
| spellingShingle | Minxue Zheng Han Hu Yue Niu Qiu Shen Feng Jia Xiaolei Geng Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods European Journal of Remote Sensing Yield prediction machine learning solar-induced chlorophyll fluorescence winter wheat vegetation indices |
| title | Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods |
| title_full | Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods |
| title_fullStr | Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods |
| title_full_unstemmed | Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods |
| title_short | Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods |
| title_sort | modeling of winter wheat yield prediction based on solar induced chlorophyll fluorescence by machine learning methods |
| topic | Yield prediction machine learning solar-induced chlorophyll fluorescence winter wheat vegetation indices |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2025.2455940 |
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