3D SAR imaging of the snowpack in presence of propagation velocity changes: Results from the AlpSAR campaign
In this paper we present results relative to the 3D GBSAR surveys acquired in february 2013 on the Austrian Alps as a part of the ESA campaign AlpSAR. The GBSAR was operated at X- and Ku-Band with a bandwidth of 4 GHz and employing a 2D synthetic aperture, resulting in 3D resolution capabilities at a resolution of few centimeters. Images produced at two different sites reveal the presence of multiple layers within the snowpack. The strongest backscatter contributions have been observed to correspond to bottom layers, that dominate the ones from the snow/air interface and the near subsurface. GBSAR data are observed to provide sensitivity to the propagation velocity into the snowpack, as revealed by the apparent depth variation with respect to the incidence angle.
SAR tomography of natural environments: Signal processing, applications, and future challenges
Synthetic Aperture Radar (SAR) Tomography (TomoSAR) provides access to the three-dimensional (3D) structure of illuminated media by jointly focusing multiple SAR acquisitions. TomoSAR imaging can be understood in simple terms by considering that multiple SAR flight lines, or orbits, allow forming a bi-dimensional synthetic aperture, resulting in the possibility to resolve the targets not only in the range-azimuth plane, but also in elevation. This simple principle brings along unprecedented possibilities, providing a way to investigate the illuminated media based on a direct observation of their vertical structure. The aim of this paper is to provide the readers with a brief tutorial on the use of TomoSAR imaging in the remote sensing of distributed media, by presenting basic imaging principles, applications, signal processing methods, and identifying challenges for future tomographic SAR systems.
Snowpack Characterization in Mountainous Regions Using C-Band SAR Data and a Meteorological Model
This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method.