Spectroscopy and Spectral Analysis, Volume. 34, Issue 5, 1352(2014)
Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index
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.
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XIE Qiao-yun, HUANG Wen-jiang, CAI Shu-hong, LIANG Dong, PENG Dai-liang, ZHANG Qing, HUANG Lin-sheng, YANG Gui-jun, ZHANG Dong-yan. Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1352
Received: Jul. 10, 2013
Accepted: --
Published Online: May. 6, 2014
The Author Email: Qiao-yun XIE (xieqiaoyun2011@gmail.com)