Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression
文献类型： 外文期刊
作者： Li, Xinchuan 1 ; Zhang, Youjing 1 ; Bao, Yansong 2 ; Luo, Juhua 3 ; Jin, Xiuliang 4 ; Xu, Xingang 4 ; Song, Xiaoyu 4 ; Ya 1 ;
作者机构： 1.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Key Lab Aerosol Cloud Precipitat China Meteorol A, Nanjing 210044, Jiangsu, Peoples R China
3.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Peoples R China
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： hyperspectral remote sensing; leaf area index (LAI); spectral feature; partial least squares regression
期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）
ISSN：
年卷期： 2014 年 6 卷 7 期
页码：
收录情况： SCI
摘要： The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge-NIR spectral region (680 nm-1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not wsignificantly improve the accuracy of the PLSR model. The PLSR model based on the fourteen features with the highest VIP values predicted LAI with a mean bootstrapped R-2 value of 0.880 and a mean RMSE of 0.943 on the validation dataset and produced an estimated LAI result better than that, including the entire 54-feature dataset with a mean R-2 of 0.875 and a mean RMSE of 0.965. The results of this study thus suggest that the use of only a few of the best features by VIP values is sufficient for LAI estimation.
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