Summary: | A key uncertainty in mass balance studies of glaciers and ice sheets is still today the density for the volume-to-mass conversion. This is not only reported on a global scale [1] but also for recent local studies [2], where even the presence of in situ measurements can only partly capture the density uncertainty [3]. The volume-to-mass conversion factor can span a wide range from 0 to 2000 kg m−3 but many studies use fixed density values such as 850 ± 60 kg m−3 [4]. Therefore, there is a clear need for improved spatial and temporal information about ice sheet subsurface properties. Polarimetric and multi-baseline interferometric SAR techniques are promising tools to investigate the subsurface properties of glaciers and ice sheets, due to the signal penetration of up to several tens of meters into dry snow, firn, and ice. (Pol-)InSAR models were shown to provide information about refrozen melt layers [5] and signal extinction [6]. With TomoSAR, the imaging of subsurface features in glaciers [7], and ice sheets [8][9] was demonstrated and the effect of subsurface layers, different ice types, firn bodies, and crevasses was recognized. Such subsurface structure information can provide at most an indirect information about density and a related parameter retrieval method is missing. Further, a general challenge is the ambiguity between the depth of scatterers and the density, because the density determines the permittivity which is required to account for the slower signal propagation speed in the subsurface. One way of addressing this is the integration of polarimetric measurements. PolSAR models provide a link between the co-polarization HH-VV phase difference (CPD) and the dielectric anisotropy of the firn volume [10]. This modeling approach establishes a relationship of the measured CPD to firn density, firn anisotropy and the vertical backscattering distribution. The integration of vertical backscatter profiles from Pol-InSAR or TomoSAR into the PolSAR CPD model theoretically allows the inversion of firn density ...
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