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Nowadays the analysis of remote sensing data for environmental monitoring is fundamental to understand the local and global Earth dynamics. In this context, the main goal of this thesis is to present novel signal processing methods for the estimation of biophysical parameters and for the analysis icy terrain with active sensors. The thesis presents three main contributions. In the context of biophysical parameters estimation we focus on regression methods. According to the analysis of the literature, most of the regression techniques require a relevant number of reference samples to model a robust regression function. However, in real-word applications the ground truth observations are limited as their collection leads to high operational cost. Moreover, the availability of biased samples may result in low estimation accuracy. To address these issues, in this thesis we propose two novel contributions. The first contribution is a method for the estimation of biophysical parameters that integrates theoretical models with empirical observations associated to a small number of in-situ reference samples. The proposed method computes and correct deviations between estimates obtained through the inversion of theoretical models and empirical observations. The second contribution is a semisupervised learning (SSL) method for regression defined in the context of the ε-insensitive SVR. The proposed SSL method aims to mitigate the problems of small-sized biased training sets by injecting priors information in the initial learning of the SVR function, and jointly exploiting labeled and unlabeled samples in the learning phase of the SVR. The third contribution of this dissertation addresses the clutter detection problem in radar sounder (RS) data. The capability to detect clutter is fundamental for the interpretation of subsurface features in the radargram. In the state of the art, techniques that require accurate information on the surface topography or approaches that exploit complex multi-channel radar sounder systems have been presented. In this thesis, we propose a novel method for clutter detection that is independent from ancillary information and limits the hardware complexity of the radar system. The method relies on the interferometric analysis of two-channel RS data and discriminates the clutter and subsurface echoes by modeling the theoretical phase difference between the cross-track antennas of the RS. This allows the comparison of the phase difference distributions of real and simulated data. Qualitative and quantitative experimental results obtained on real airborne SAR and RS data confirm the effectiveness of the proposed methods.

We propose a strategy to evaluate the performance of a radar sounder for the subsurface exploration of the Europa icy crust and, in particular, the possibility to detect liquid water at the base of the ice shell. The approach integrates the information coming from experimental measurements of the dielectric properties of icy materials, thermal models related to different crustal scenarios, and numerical simulations of radar signal propagation. The radar response has been evaluated in terms of cumulative attenuation, signal-to-noise ratio (SNR), and reflectivity. Our simulations indicate that a subsurface radar operating at 9 MHz can identify shallow-buried targets and to detect the ice/water interface in various thermal scenarios. Under our assumptions the ice/water interface can be detected almost down to a depth of 15 km under a conductive ice shell, whereas for a convective ice shell, the maximum depth is about 12 km (in the cold downwelling plume). We also discuss the possibility to detect shallow targets associated with interfaces between pure water ice and $\text{MgSO}_{4} \cdot 11 \text{H}_{2}{\rm O}$ ice mixtures at various salt contents, using the data of laboratory dielectric measurements.