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 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 significantly 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 R2 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 R2 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.
Highlights
Defined as one-half of the total green leaf area per unit ground surface area [1], the leaf area index (LAI) is an important parameter and is commonly applied in environmental studies examining growth monitoring, yield estimation, evapotranspiration, radiation extinction, carbon cycling and climate [2,3,4,5]
We studied the correlations between 24 vegetation indices (VIs) and LAI (Table 6), with the results indicating significant relationships existing between LAI and all spectral parameters
In order to select suitable spectral features for LAI estimation, different features based on spectral wavebands, spectral positions and vegetation indices were evaluated, with all exhibiting the same changing tendency in two years of hyperspectral data
Summary
Defined as one-half of the total green leaf area (all-sided) per unit ground surface area [1], the leaf area index (LAI) is an important parameter and is commonly applied in environmental studies examining growth monitoring, yield estimation, evapotranspiration, radiation extinction, carbon cycling and climate [2,3,4,5]. Remote sensing techniques have been recognized as a reliable method to provide a fast, non-destructive and relatively cheap way to measure LAI on different scales [6,7]. Hyperspectral remote sensing can produce hundreds or even thousands of narrow, contiguous spectral bands, which may provide crucial additional information, potentially representing a significant improvement over broad bands in quantifying biophysical and biochemical variables, such as LAI [8,9]. Hyperspectral data are much more complex than multispectral data. They provide a vast amount of information, most adjacent wavebands are redundant and often highly correlated [10].
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