Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies

文献类型： 外文期刊

第一作者： Dong, Yingying

作者： Dong, Yingying;Wang, Jihua;Wang, Huifang;Li, Cunjun;Yang, Guijun;Wang, Qian;Liu, Feng;Zhao, Jinling;Huang, Wenjiang;Huang, Wenjiang

作者机构：

关键词： Data assimilation;Ensemble Kalman Filtering (EnKF);Four Dimensional Variational Data Assimilation (4DVAR);Leaf Area Index (LAI);Very Fast Stimulated Annealing (VFSA)

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2013 年 6 卷 1 期

页码：

收录情况： SCI

摘要： Data assimilation as an approach for crop Leaf Area Index (LAI) estimation has been rapidly developed in the field of agricultural remote sensing. Many studies have attempted to integrate sequential remotely sensed observations in the dynamical operation of physical models, aiming to improve model performance of LAI estimation by using various data assimilation schemes. In this study, a new data assimilation algorithm is proposed. For this algorithm, the background field of free parameters is constructed according to the ensemble construction scheme of EnKF algorithm, and a cost function is constructed based on the cost function construction strategy in 4DVAR algorithm in order to analyse and update the free parameters. The last updated free parameters are input into the model for LAI estimation until all the observations are assimilated. Additionally, the cost function in data assimilation procedure is optimised using VFSA algorithm. Winter wheat in Beijing in 2002 is selected as the experimental object. The crop growth model CERES-Wheat and the radiative transfer model PROSAIL are coupled in the assimilation process. Sequentially observed NDVI of winter wheat are assimilated into the coupled model using different assimilation algorithms to quantitatively compare and analyse the LAI estimation results. The performance of the new algorithm is better than CERES-Wheat model, EnKF algorithm, and 4DVAR algorithm, with a lesser RMSE by 43.68%, 41.67%, and 28.99%, respectively, and with increased R-2 by 110.53%, 90.48%, and 33.33%, respectively. Moreover, for the subset LAI >= 3.00, higher estimation precision and greater efficiency of the new algorithm are obtained.

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