Alternating Least-Squares unmixing for the extraction of sub-pixel information from agricultural areas

摘要

Multivariate Curve resolution - Alternating Least Squares (ALS) is a blind source separation method commonly used in Chemometrics to simultaneously estimate the absorption spectrum and concentration of the different components in a chemical sample. In this study, the transferability of ALS from Chemometrics to agricultural Remote Sensing is evaluated. Traditional unmixing techniques only allow estimating the sub-pixel cover distribution of the different components, but fail to provide an estimate of the pure spectral signature of the crop. This info is, however, highly valuable as this pure crop signature could be used to monitor the health status of the trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of the different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of the different components in a mixed image pixel. The ALS model was tested on simulated hyperspectral images of Citrus orchards. Both the accuracy of the extracted cover fractions and the pure spectral signatures of the crop were assessed, as well as the accuracy with which the biophysical parameters of the trees (i.e. chlorophyll content, leaf water content and Leaf Area Index) could be derived from the extracted crop signature. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE = 0.05), as well as the pure spectral signatures of the different endmembers (RRMSE ＜ 0.12), and considerably improved the extraction of biophysical parameters (ΔR~2 up to 0.43). ALS thus provides a promising new spectral decomposition tool for agricultural remote sensing.