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《中国科学:生命科学(英文版)》 2011-03
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Estimating biophysical parameters of rice with remote sensing data using support vector machines

YANG XiaoHua 1,2,HUANG JingFeng 1,WU YaoPing 2,WANG JianWen 2,WANG Pei 3,WANG XiaoMing 2 & Alfredo R.HUETE 4 1 Institute of Remote Sensing & Information Application,Zhejiang University,Hangzhou 310029,China;2 Meteorological,Hydrographic,Spatial & Synoptic Central Station of General Staff Headquarters,Beijing 100081,China;3 State Key Laboratory of Earth Surface Processes & Resource Ecology,Beijing Normal University,Beijing 100875,China;4 Department of Soil,Water,and Environmental Science,University of Arizona,Tucson,AZ 85721,USA  
Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.
【Fund】: supported by the National Natural Science Foundation of China(Grant Nos. 40571115 and 40271078);; the National Hi-Tech Research and Development Program of China(Grant No. 2006AA10Z203)
【CateGory Index】: S511
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