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Journal of Integrative Agriculture  2017, Vol. 16 Issue (10): 2283-2299    DOI: 10.1016/S2095-3119(16)61351-5
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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
LI He1, JIANG Zhi-wei1, 2, CHEN Zhong-xin1, REN Jian-qiang1, LIU Bin1, Hasituya
1 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 National Meteorological Information Center, China Meteorological Administration, Beijing 100081, P.R.China
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Abstract  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 (R2) 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 R2 of 0.81, an RMSE of 362 kg ha–1, and an RE of 4.52% were obtained at the pixel scale of 30 m×30 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. 
Keywords:  winter wheat yield estimates        crop model        data assimilation        ensemble Kalman filter        uncertainty        leaf area index  
Received: 25 January 2016   Accepted: 30 September 2017

This work was supported by the National Natural Science Foundation of China (41401491, 41371396, 41301457, 41471364), the Introduction of International Advanced Agricultural Science and Technology, Ministry of Agriculture, China (948 Program, 2016-X38), and the Agricultural Scientific Research Fund of Outstanding Talents and the Open Fund for the Key Laboratory of Agri-informatics, Ministry of Agriculture, China (2013009).

Corresponding Authors:  Correspondence CHEN Zhong-xin, Tel: +86-10-82105089, E-mail:   
About author:  LI He, Mobile: +86-18811153465, E-mail:

Cite this article: 

LI He, JIANG Zhi-wei, CHEN Zhong-xin, REN Jian-qiang, LIU Bin, Hasituya . 2017. 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. Journal of Integrative Agriculture, 16(10): 2283-2299.

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