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Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index

文献类型： 外文期刊

作者： Xie Qiao-yun 1 ; Huang Wen-jiang 1 ; Cai Shu-hong 3 ; Liang Dong 2 ; Peng Dai-liang 1 ; Zhang Qing 1 ; Huang Lin-sheng 2 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China

2.Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230039, Peoples R China

3.Hebei Agr Tech Extens Stn, Shijiazhuang 050011, Peoples R China

4.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Leaf area index;Hyperspectral;Support vector machine;Wavelet transform;Principle component analysis

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2014 年 34 卷 5 期

页码：

收录情况： SCI

摘要： The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed. Both discrete wavelet transform method and principal component analysis method are based on feature extraction and data dimension reduction, and multivariate regression models of the two methods showed comparable accuracy (R-2 of DWT and PCA model was 0. 697 1 and 0. 692 4 respectively; RMSE was 0. 605 8 and 0. 554 1 respectively). While the model based on continuous wavelet transform suffered the lowest accuracy and didn't seem to be qualified to inverse LAI It was indicated that the nonlinear regression model with support vector machines method is the most eligible model for estimating winter wheat LAI in the study area.

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