This letter deals with the estimation of the refractive index of snowpack based on particle swarm optimization (PSO) algorithm. By leveraging its ability to handle multidimensional optimization problems, PSO is used both to compute the shortest path between the synthetic aperture radar tomographic system antenna and the imaged snowpack scatterers, taking into account its permittivity and to optimize the difference in depth coordinates between the snowpack tomogram and a simulated one using the same system configuration and measurement parameters. The proposed method is applied on two measured tomogram, namely, those whose data were acquired during a measurement campaign carried out in the French Alps.

This letter introduces an iterative technique for estimating the refractive indices of a multilayered snowpack. It is based on minimizing the difference between the tilt observed in a TomoSAR image, assuming free-space propagation, and the one obtained using a developed analytical modeling approach. This minimization is carried out using particle swarm optimization (PSO) algorithm, and the subsequent approach applies an analytical modeling method that combines geometric principles and Snell–Descartes law to relate the apparent positions of scatterers to their real positions. In addition, it takes also into account the transmitting and receiving antenna coordinates and medium properties such as thickness and wave propagation velocity. This study is further enhanced by calculating cumulative and normalized intensities, coherence, and phase center. This analysis offers valuable insights into the depth of penetration and the layers that prevent the electromagnetic waves propagation from penetrating them.

This paper presents a method for multi-layered snowpack vertical profile characterization using a low complexity portable MIMO bistatic radar system. An iterative procedure based on calculating the electromagnetic distance which takes into account the refractive index of each layer is proposed. It estimates the refractive index from the snowpack top to the bottom layers. Furthermore, experimental radar measurements performed at the Col de Porte, in the french Alps, is backed up by in-situ stratigraphic samplings provided by Météo France and which are used as a reference. The obtained results are anaylsed and discussed.

The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.

The reconstruction of high-elevation natural and artificial structures through Synthetic Aperture Radar (SAR) tomography has been an active research topic owing to its significance in various earth science applications. However, the complexity of this task arises from inaccuracies in the estimated reconstruction, attributed to factors such as low signal-to-noise ratios, decorrelations, few and uneven measurements, and overlapping scatterers. The utilization of iterative spectral estimation methods has been demonstrated to be beneficial in addressing some of these inaccuracies. Thus, selecting the best method within this class constitutes a challenge. In this context, our letter aims to propose a generalized formula linking the maximum likelihood-based iterative methods via a regularization parameter. The behavior of the latter is analyzed for several values in order to unveil the potential of the proposed approach in achieving a balance between noise reduction and detection performance. The experimental study has been conducted on simulated and real SAR data acquired by airborne and spaceborne systems covering tropical forest and build-up areas. The obtained results show the effectiveness and performance of the optimal regularization parameter to eliminate noise while preserving scatterers’ contribution.

This paper proposes to use ground-based high-resolution 3-D radar imaging, in order to study the interaction between electromagnetic waves and snow packs, and to provide a physical interpretation for the reflectivity of Sentinel 1 images over snow covered regions. Preliminary results show that, according to usually assumed behaviors, fresh snow has an extremely low reflectivity at C band, and wet snow does not let waves go through. Between these two extreme configurations, this study reveals that snow packs may have complex and significant scattering patterns, mainly due to the presence of transformed snow and of rough interfaces between the layers, again due to transformation phenomena.

This paper presents experimental results of the 3-D characterization of alpine snowpacks, obtained using a low complexity portable MIMO radar system that operates at C-band. Different types of snow at different altitudes and seasons are studied. The acquired datasets are processed using the Back Projection Algorithm and the resulting tomograms are compared to Météo France ground measurements (Snow Micro Pen transects, density and stratigraphy profiles and liquid water content). The obtained tomograms show that the system mostly detects melt forms and faceted crystals. These measurements provide 3-D electromagnetic ground truth that can be used to confirm the results obtained by Sentinel-1.

This paper evaluates the potential of Polarimetric SAR Tomography (PolTomSAR) for analyzing a semi-opaque Random Volume lying over a rough Ground (RVoG), and to assess different characterization methods aiming to retrieve the ground characteristics. This study is based on the use of controlled experiments, during which a miniaturized RvoG-like scene is imaged with a laboratory 3-D SAR, operated along a 2-D aperture. Polarimetric and tomographic signals are acquired for various configurations, and different scattering mechanisms are naturally captured by removing, hiding or replacing different parts of the scene. This unique possibility to isolate specific scattering mechanisms is used to evaluate the performance and relevance of existing decomposition techniques generally applied to forest characterization.