  With current and upcoming imaging spectrometers, automated band analysis
techniques are needed to enable efficient identification of most informative
bands to facilitate optimized processing of spectral data into estimates of
biophysical variables. This paper introduces an automated spectral band
analysis tool (BAT) based on Gaussian processes regression (GPR) for the
spectral analysis of vegetation properties. The GPR-BAT procedure sequentially
backwards removes the least contributing band in the regression model for a
given variable until only one band is kept. GPR-BAT is implemented within the
framework of the free ARTMO's MLRA (machine learning regression algorithms)
toolbox, which is dedicated to the transforming of optical remote sensing
images into biophysical products. GPR-BAT allows (1) to identify the most
informative bands in relating spectral data to a biophysical variable, and (2)
to find the least number of bands that preserve optimized accurate predictions.
This study concludes that a wise band selection of hyperspectral data is
strictly required for optimal vegetation properties mapping.
