Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

Abstract: 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 se lected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various grow th stages during 2008 and 2009. We extracted hyperspectral featur es 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 (

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