Progress in Emulation For Radiative Transfer Modeling And Mapping

Abstract

Physical radiative transfer models (RTMs) of leaf and canopies with sufficient realism enable the retrieval of biophysical variables from imaging spectroscopy through numerical inversion. However, advanced RTMs are computationally intensive, which hampers practical applicability of inversion schemes against remote sensing images. To bypass the computational load such RTMs, it has been proposed to approximate these models by means of statistical learning, i.e. emulation. Here we tested three machine learning regression algorithms, i.e. neural networks, kernel ridge regression and Gaussian processes regression, on their ability to emulate the advanced RTM SCOPE (Soil-Canopy-Observation of Photosynthesis and the Energy balance) for limited set of input variables. The best performing emulator was implemented into a numerical inversion scheme to process a subset of an hyperspectral image into a multitude of vegetation properties. Obtained maps are not only consistent, but also processing time was in the order of minutes - in comparison, by using SCOPE the processing would have taken days.