Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index
XIE Qiao-yun1, 2, HUANG Wen-jiang1*, CAI Shu-hong3, LIANG Dong2, PENG Dai-liang1, ZHANG Qing1, HUANG Lin-sheng2, YANG Gui-jun4, ZHANG Dong-yan2
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. Key Laboratory of Intelligent Computer & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China 3. Hebei Agricultural Technique Extension Station, Shijiazhuang 050011, China 4. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Abstract：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 (R2 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.
Key words：Leaf area index;Hyperspectral;Support vector machine;Wavelet transform;Principle component analysis
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