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

作     者：LI He JIANG Zhi-wei CHEN Zhong-xin REN Jian-qiang LIU Bin Hasituya 

作者机构：Key Laboratory of Agricultural Remote SensingMinistry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information CenterChina Meteorological Administration 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报（英文版）)

年 卷 期：2017年第16卷第10期

页      面：2283-2299页

核心收录：

学科分类：0710[理学-生物学] 0832[工学-食品科学与工程（可授工学、农学学位）] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 0905[农学-畜牧学] 09[农学] 0906[农学-兽医学] 0901[农学-作物学] 0703[理学-化学] 0902[农学-园艺学] 0713[理学-生态学] 

基　　金：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) the Agricultural Scientific Research Fund of Outstanding Talents the Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009) 

主　　题：winter wheat yield estimates crop model data assimilation ensemble Kalman filter uncertainty leaf area index 

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. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively 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 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.