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International audience; Synthetic aperture radar (SAR) tomography (TomoSAR) is an emerging technology to image the 3D structure of the illuminated media. 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. 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. 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. 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. We will introduce the basic physical principles behind TomoSAR, present the most relevant experimental results of the last decade, and discuss the potentials of BIOMASS tomography.

International audience; Researches carried out in the last years have shown that the use of P-band SAR tomography (TomoSAR) largely improves the retrieval of the above-ground biomass (AGB) in tropical forests, providing a most encouraging element toward the systematic employment of TomoSAR techniques in the frame of the upcoming spaceborne mission BIOMASS. All of these researches were carried out using campaign data acquired in a single day, and under stable (mostly sunny) weather conditions. The impact of temporal decorrelation was considered in the literature by analyzing ground-based Radar data from the TropiSCAT campaign (Paracou, French Guiana), and found not to be a show-stopper for BIOMASS tomography. Yet, the validity of this analysis was limited to sunny days only. Accordingly, a precise assessment of the impact of changing weather conditions on TomoSAR is currently missing. The aim of this paper is to provide a first experimental element to fill this gap. To do this, data from the TropiSCAT archive are reprocessed to mimic BIOMASS repeat-pass tomography. Since BIOMASS tomography will be implemented by taking seven acquisitions with a revisit time of three days, we form tomograms by taking two TropiSCAT antennas every three days (and three antennas on the last day), which means that any single tomogram is actually obtained by mixing seven different days and under different weather conditions. The quality of tomographic imaging is then assessed by evaluating the observed backscattered power fluctuations in the tomogram time series. While imaging quality is observed to degrade by mixing different days, the resulting temporal variations of the backscattered power in the canopy layer are within 1.5-dB rms in cross polarization. For this forest site, this error is translated into an AGB error of about 50-100 t/ha, which is 20% or less of forest AGB. In Special issue on forest structure estimation in remote sensing.

With reference to the application to the imaging and monitoring of infrastructures and buildings in urban areas, SAR tomography has been mainly developed and tested at full resolution. In this work, we investigate the possibility related to the use of a multilook approach for fine resolution analysis of ground structures that combines SAR tomography and a method, CAESAR, recently proposed for classical DInSAR analysis at coarse resolution over large areas. Shown results, achieved by processing two 3 m spatial resolution (stripmap mode) COSMO-SKYMED datasets relative to the urban areas of Naples and Rome (Italy), clearly indicate that the proposed multilook-based method allows achieving an impressive density of detected scatterers over buildings and infrastructures, much higher than those achievable with standard full-resolution methods.

Synthetic aperture radar (SAR) data processed with interferometric techniques are widely used today for environmental risk monitoring and security. SAR tomography techniques are a recent advance that provide improved three-dimensional (3-D) reconstruction and long-term deformation monitoring capabilities. This article is meant to discuss the main developments achieved in the last few years in the SAR tomography framework, with particular reference to both urban and forest scenarios. An insight on classical multipass interferometric processing is also included to summarize the importance of the technology for natural hazards monitoring and to provide the basis for the description of SAR tomography.

International audience; This paper presents some processing techniques for 3-D imaging using tomographic SAR data sets acquired using a polarization diversity. Somme application of these approaches to 3-D mapping of urban areas, tropical forest characterization and under-foliage concealed target detection are given. © 2016 European Association of Antennas and Propagation.

The processing of data acquired by Synthetic Aperture Radar (SAR) with advanced methods based on multidimensional imaging opens new perspectives in the monitoring of facilities in built environment. The availability of data acquired by recent meter-resolution satellite SAR sensors allows precise monitoring of even single buildings and infrastructures, therefore valuably helping in the detection and monitoring of exposed facilities. This paper deals with the potentialities offered by the use of SAR data in the framework of the built environment monitoring by discussing results obtained by the processing of Cosmo-Skymed data.

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space/deformation-velocity (4-D) analysis. It extends SAR interferometry and differential interferometry concepts, offering new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting scatterers showing partial correlation properties induced by simultaneous acquisitions from satellite formations or an uneven temporal distribution of satellite constellations. To this end, we first provide a general model for the pixel imaged by a multidimensional SAR system. Then, we design a constant false alarm rate (CFAR) decision rule accounting for the presence of partially coherent scatterers. At the analysis stage, we assess the performance of the new detector also in comparison with a previously proposed CFAR scheme, developed in the context of SAR tomography for fully coherent scatterers.