Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics
文献类型：期刊论文
| 作者 | Li, Shiji3; Huang, Jianxi2,3; Xiao, Guilong3; Huang, Hai3; Sun, Zhigang1; Li, Xuecao2,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 16期号:17页码:18 |
| 关键词 | winter wheat yield estimation machine learning vegetation indices phenological metrics |
| DOI | 10.3390/rs16173217 |
| 产权排序 | 3 |
| 英文摘要 | Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking. |
| WOS关键词 | LEAF-AREA INDEX ; ESTIMATION MODEL ; SPECTRAL INDEX ; CROP ; CORN ; PREDICTION ; SATELLITE ; NDVI ; WOFOST ; LAI |
| 资助项目 | the National Natural Science Foundation of China[42271339] ; National Natural Science Foundation of China |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001311638000001 |
| 出版者 | MDPI |
| 资助机构 | the National Natural Science Foundation of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/208614]  |
| 专题 | 禹城站农业生态系统研究中心_外文论文 |
| 通讯作者 | Huang, Jianxi |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China 3.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Shiji,Huang, Jianxi,Xiao, Guilong,et al. Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics[J]. REMOTE SENSING,2024,16(17):18. |
| APA | Li, Shiji,Huang, Jianxi,Xiao, Guilong,Huang, Hai,Sun, Zhigang,&Li, Xuecao.(2024).Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics.REMOTE SENSING,16(17),18. |
| MLA | Li, Shiji,et al."Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics".REMOTE SENSING 16.17(2024):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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