This paper aims at providing the reader with a comprehensive understanding of TomoSAR and its application for remote sensing of forested areas, with special attention to the case of tropical forests. The results obtained in the frame of these studies demonstrated that TomoSAR can be used for accurate retrieval of geophysical variables such as forest height and terrain topography and, especially in the case of dense tropical forests, to provide a more direct link to AGB. The research on the use of P-band waves, in particular, has been largely propelled since 2007 in experimental studies supporting the future spaceborne Mission BIOMASS, to be launched in 2022 with the aim of mapping forest aboveground biomass (AGB) accurately and globally. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. Synthetic aperture radar (SAR) tomography (TomoSAR) is an emerging technology to image the 3D structure of the illuminated media. Comparison with advanced interferometry methods is discussed, and real forest data Diff-Tomo tests of deformation monitoring, currently at P-band, are presented. Diff-Tomo subcanopy deformation (subsidence or deep landslide) monitoring is explored by simulated analyses of an L-band scenario system tradeoffs in resorting to higher or lower carrier frequency are also hinted. This potential is tested applying the Generalized-MUSIC/-Capon methods for this challenging application, the former being expected to be more accurate when correctly matched and the latter more flexible. In this paper, the feasibility of exploiting the Diff- Tomo framework for decoupling the interfering canopy scatterers in the retrieval of surface deformation in vegetated areas is analysed, expanding first trials in. After the related first Diff- Tomo processor, full model-based Generalized-MUSIC, a semi-parametric Generalized-Capon processor has been conceived and tested for the non-stationary distributed scenarios. More recently, through Diff-T omo, identifying spatial (height)-temporal spectra of multiple height-distributed decorrelating (forest) scatterers, an advanced decorrelation-robust T omography has been obtained, together with separation of decorrelation mechanisms in forest layers. Differential SAR Tomography (Diff-Tomo) has emerged as a powerful crossing of Differential Interferometry and 3D Tomography, producing 4D (3D+Time) imaging capabilities extensively applied to urban monitoring.
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