In the current context of intense scientific discussion about the extent of climate change, the need to thoroughly monitor natural processes is constantly raised: a better observation capability is required to solve pressing scientific questions, such as a deeper understanding of Earth dynamics and the impact that the human activity is having on them. Nowadays though, these processes are only selectively observed at low temporal and spatial resolution and regular and global scale observations are missing for key Earth system parameters such as global vegetation biomass, glacial motion or freezing and thawing cycles of the permafrost. Besides, in the recent years, the importance to move from 2D to 3D observations has been stressed, since it has been demonstrated that the horizontal distribution is not sufficient for an appropriate assessment of a number of physical parameters of interest. Since Synthetic Aperture Radar (SAR) signals at low frequency can penetrate to a certain extent forest and ice bodies, they allow through advanced imaging techniques the extraction of relevant information related to the 3D structural parameters of the observed scene. In such a context, the objectives of this project are two-fold. First, a novel imaging approach is proposed to improve the quality of the 3D structure information retrieved by SAR systems in forest and ice scenarios. Then, the aim is to establish tools (currently missing) to bridge the gap between the SAR observations and the development of novel bio/geophysical information products in the framework of forest and ice monitoring. Synergies between different disciplines (engineering, signal processing environmental and climate studies) are necessary to translate SAR reflectivity observables into high quality estimations of quantitative environmental variables. This will contribute to establish earth observation systems as a long term operational source of information of global natural processes and ecosystem change.
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