文献类型： 外文期刊

第一作者： Cao, Junjun

作者： Cao, Junjun;Wang, Huijing;Cao, Junjun;Li, Jinxiao;Tian, Qun;Niyogi, Dev;Niyogi, Dev;Niyogi, Dev

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关键词： yield forecasting; climate variables; subseasonal-to-seasonal prediction; machine learning; winter wheat

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

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年卷期： 2022 年 14 卷 7 期

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收录情况： SCI

摘要： Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML-dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R-2 of 0.85 and RMSE of 0.78 t/ha 3-4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

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