A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge  

作　　者：ZHU Xiaohua ZHAO Yingshi FENG Xiaoming 

机构地区：[1]Key Laboratory of Quantitative Remote Sensing Information Technology,Academy of Opto-Electronics,Chinese Academy of Sciences [2]Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences [3]University of Chinese Academy of Sciences [4]Research Center of Eco-environmental Science,Chinese Academy of Sciences

出　　处：《Chinese Geographical Science》2013年第5期550-561,共12页中国地理科学（英文版）

基　　金：Under the auspices of Major State Basic Research Development Program of China(No.2007CB714407);National Natural Science Foundation of China(No.40801070);Action Plan for West Development Program of Chinese Academy of Sciences(No.KZCX2-XB2-09)

摘　　要：In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.In this paper, a methodology for Leaf Area Index （LAI） estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge. Firstly, sensitive parameters of crop model were calibrated by Shuffled Complex Evo- lution method developed at the University of Arizona （SCE-UA） optimization method based on phenological information, which is called temporal knowledge. The calibrated crop model will be used as the forecast operator. Then, the Taylor＇s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer （MODIS） multi-scale data, which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model （ACRM） model. The calibrated LAI result was used as the observation operator. Finally, an Ensemble Kalman Filter （EnKF） was used to assimilate MODIS data into crop model. The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products. The root mean square error （RMSE） of LAI calcu- lated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation （0.3795）, and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265. All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

关 键 词：ASSIMILATION temporal and spatial knowledge Leaf Area Index （LAI） crop model Ensemble Kalman Filter （EnKF） 

分 类 号：S127[农业科学—农业基础科学]