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Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

文献类型： 外文期刊

作者： Li Rui 1 ; Li Cun-jun 1 ; Dong Ying-ying 1 ; Liu Feng 1 ; Wang Ji-hua 1 ; Yang Xiao-dong 1 ; Pan Yu-chun 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Capital Normal Univ, Inst Informat Engn, Beijing 100048, Peoples R China

3.Chengdu Dawan High Sch, Chengdu 610300, Peoples R China

关键词： crop model;assimilation;Ensemble Kalman Filter algorithm;leaf area index

期刊名称：AGRICULTURAL SCIENCES IN CHINA （ 影响因子：0.82； 五年影响因子：0.997 ）

ISSN： 1671-2927

年卷期： 2011 年 10 卷 10 期

页码：

收录情况： SCI

摘要： Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R(2) reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

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