Abstract
This paper describes a sensitivity study performed on simulated radar and optical remote sensing forest data. It presents how the dual model has been built up. The first step is a forest growth model fed with biophysical parameters. The geometrical description is then the input of an optical hyperspectral model, giving reflectance spectra, and a Synthetic Aperture Radar (SAR) model, giving the polarimetric and interferometric observables. As an illustration, the first results obtained by both models outputs are presented, and fusions of these outputs are performed.
Highlights
The retrieval of biophysical parameters of forests with remote sensing is nowadays a challenge
This paper describes a sensitivity study performed on simulated radar and optical remote sensing forest data
We can see a good separation of soil moisture content and volume moisture content for very low biomass. This can be explained by the fact that after having found the first axis that will maximize the separation of data along the biomass parameter, the canonical analysis will act like a Principal Component Analysis (PCA) and will find the effects of the two other parameters in the data
Summary
The retrieval of biophysical parameters of forests with remote sensing is nowadays a challenge. Spectral signatures provided by optical measurements are able to deliver features of forest vegetation, like Leaf Area Index (LAI) [2], to derive optical indexes like the Normalized Difference Vegetation Index (NDVI), and can be used to determinate tree species [3]. Using both sources of information through a combination process should improve the determination of the characteristic parameters of forest [4]. The results carried out on previous radar and optical data, of a nonsupervised and of a supervised data analyses, are, respectively, shown in Sections 5 and 6
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