DocumentCode :
32335
Title :
Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies
Author :
Yingying Dong ; Jihua Wang ; Cunjun Li ; Guijun Yang ; Qian Wang ; Feng Liu ; Jinling Zhao ; Huifang Wang ; Wenjiang Huang
Author_Institution :
Inst. of Agric. Remote Sensing & Inf. Syst. Applic., Zhejiang Univ., Hangzhou, China
Volume :
6
Issue :
1
fYear :
2013
fDate :
Feb. 2013
Firstpage :
188
Lastpage :
201
Abstract :
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 R2 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.
Keywords :
crops; data assimilation; geophysical signal processing; radiative transfer; remote sensing; variational techniques; 4DVAR algorithm; AD 2002; Beijing; CERES-Wheat model; China; EnKF algorithm; LAI estimation; PROSAIL model; VFSA algorithm; agricultural remote sensing; cost function construction strategy; crop canopies; data assimilation algorithm; free parameters; leaf area index; radiative transfer model; winter wheat; Agriculture; Algorithm design and analysis; Biological system modeling; Data assimilation; Data models; Estimation; Remote sensing; Data assimilation; Ensemble Kalman Filtering (EnKF); Four Dimensional Variational Data Assimilation (4DVAR); Leaf Area Index (LAI); Very Fast Stimulated Annealing (VFSA);
fLanguage :
English
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher :
ieee
ISSN :
1939-1404
Type :
jour
DOI :
10.1109/JSTARS.2012.2208943
Filename :
6268294
Link To Document :
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