Extraction of foliar biochemistry from hyperspectral data using wavelet decomposition

This study explores the potential of wavelet decomposition of leaf reflectance spectra for quantifying foliar biochemicals and water. A leaf-scale radiative transfer model was used to generate a very large spectral data set with which to develop and rigorously test the technique. The size of the data set enabled a thorough statistical analysis of the performance a range of alternative methods for constructing predictive models including the selection of specific wavelet functions, continuous or discrete transforms, reflectance or derivative input spectra and number of wavelet coefficients used as predictors. The results demonstrated that wavelet decomposition techniques can generate accurate predictions of protein, lignin/cellulose and water content, despite wide variations in of all of the biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and enabled the greatest improvements in performance for protein followed by lignin/cellulose then water content. Hence, the study highlights the capabilities of wavelet decomposition for extracting information concerning leaf components that have narrow, weak absorption features, which are otherwise difficult to characterise in untransformed reflectance spectra.