Methods for the analysis of time series of multispectral remote sensing images and application to climate change variable estimations

In the last decades, the increasing number of new generation satellite images characterized by a better spectral, spatial and temporal resolution with respect to the past has provided unprecedented source of information for monitoring climate changes.To exploit this wealth of data, powerful and automatic methods to analyze remote sensing images need to be implemented. Accordingly, the objective of this thesis is to develop advanced methods for the analysis of multitemporal multispectral remote sensing images to support climate change applications. The thesis is divided into two main parts and provides four novel contributions to the state-of-the-art. In the first part of the thesis, we exploit multitemporal and multispectral remote sensing data for accurately monitoring two essential climate variables. The first contribution presents a method to improve the estimation of the glacier mass balance provided by physically-based models. Unlike most of the literature approaches, this method integrates together physically-based models, remote sensing data and in-situ measurements to achieve an accurate and comprehensive glacier mass balance estimation. The second contribution addresses the land cover mapping for monitoring climate change at high spatial resolution. Within this work, we developed two processing chains: one for the production of a recent (2019) static high resolution (10 m) land cover map at subcontinental scale, and the other for the production of a long-term record of regional high resolution (30 m) land cover maps. The second part of this thesis addresses the common challenges faced while performing the analysis of multitemporal multispectral remote sensing data. In this context, the third contribution deals with the multispectral images cloud occlusions problem. Differently from the literature, instead of performing computationally expensive cloud restoration techniques, we study the robustness of deep learning architectures such as Long Short Term Memory classifier to cloud cover. Finally, we address the problem of the large scale training set definition for multispectral data classification. To this aim, we propose an approach that leverages on available low resolution land cover maps and domain adaptation techniques to provide representative training sets at large scale. The proposed methods have been tested on Sentinel-2 and Landsat 5, 7, 8 multispectral images. Qualitative and quantitative experimental results confirm the effectiveness of the methods proposed in this thesis.