Abstract

The retrieval of geo-/bio-physical variables from remote sensing imagery is a challenging and important research field. On the one hand, advances in electronics, engineering and space sciences are offering to the users community new sensors capable to acquire information on the Earth surface with higher accuracy and improved features with respect to the past. On the other hand, the need of large-scale, accurate and up-to-date mapping and monitoring of natural targets and physical processes is becoming fundamental for many application domains. This calls for the development of accurate, robust and effective retrieval methodologies. The main goal of this thesis is to investigate and develop advanced methods and systems for the retrieval of geo-/bio-physical variables from satellite remote sensing imagery being able to exploit the potential of new and upcoming satellite systems and support real application domains. Special attention has been devoted to the definition of methods and to the analysis of data acquired in the challenging mountain environment. The activity carried out and presented in this dissertation is oriented to investigate the main limitations of the existing methodologies for addressing the estimation problem and to develop novel and improved systems that can overcome the drawbacks identified. In particular, the following main novel contributions are proposed in this thesis: a) A theoretical and empirical comparative analysis of non-linear machine learning regression methods, namely the Multi-Layer Perceptron Neural Network and the Support Vector Regression, for soil moisture retrieval in different operational scenarios. b) A novel multi-objective model-selection strategy for tuning the free parameters of non-linear regression methods taking into account different quality metrics that are jointly optimized. c) A novel hybrid approach to the retrieval of geo-/bio-physical variables from remote sensing data integrating both theoretical electromagnetic models and field reference measurements. d) A sensitivity analysis and a retrieval system for soil moisture content estimation from new generation SAR imagery in an Alpine catchment. e) An empirical study on the effectiveness of fully-polarimetric SAR signals for soil moisture estimation in mountain areas. f) An improved algorithm for mapping and monitoring Green Area Index (GAI) in Alpine pastures and meadows from satellite MODIS imagery. Qualitative and quantitative experimental results obtained on real remotely sensed data confirm the effectiveness of the proposed solutions.