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Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

文献类型： 外文期刊

作者： Li He 1 ; Jiang Zhi-wei 1 ; Chen Zhong-xin 1 ; Ren Jian-qiang 1 ; Liu Bin 1 ; Hasituyu 1 ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr, Key Lab Agr Remote Sensing, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

2.China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China

关键词： winter wheat yield estimates;crop model;data assimilation;ensemble Kalman filter;uncertainty;leaf area index

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2017 年 16 卷 10 期

页码：

收录情况： SCI

摘要： To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index (LAI) values. The performances of the calibrated crop environment resource synthesis for wheat (CERES-Wheat) model for two different assimilation scenarios were compared by employing ensemble Kalman filter (EnKF)-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overall, the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehensively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination (R-2) of 0.84, a root mean square error (RMSE) of 323 kg ha(-1), and a relative errors (RE) of 4.15% were obtained at the field plot scale and an R-2 of 0.81, an RMSE of 362 kg ha(-1), and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

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