The analysis of data from an airborne synthetic aperture radar (SAR) campaign in the percolation zone of Greenland revealed an interferometric coherence undulation behavior with respect to vertical wavenumber, which cannot be explained with existing models. We propose a model extension that accounts for scattering from distinct layers below the surface. Simulations show that the periodicity of the coherence undulation is mainly driven by the vertical distance between dominant subsurface layers, while the amplitude of the undulation is determined by the ratio between scattering from distinct layers and scattering from the firn volume. We use the model to interpret quad-pol SAR data at X-, C-, S-, Land nd P-band. The inferred layer depths match layer detections in ground based radar data and in situ measurements. We conclude that in the percolation zone scattering from subsurface layers has to be taken into account to correctly interpret SAR data and demonstrate the potential to retrieve geophysical information about the vertical subsurface structure.

Synthetic Aperture Radar Interferometry (InSAR) is able to provide important information for the characterization of the surface topography of glaciers and ice sheets. However, due to the inherent penetration of microwaves into dry snow, firn, and ice, InSAR elevation models are affected by a penetration bias. The fact that this bias depends on the snow and ice conditions as well as on the interferometric acquisition parameters complicates its assessment and makes it also relevant for measuring topographic changes. Recent studies indicated the potential for model based compensation of this penetration bias. This paper follows this approach and investigates the performance of two subsurface volume models for this task. Single-channel and polarimetric approaches are discussed for random and oriented volume scenarios. The model performance is assessed on two test sites in the percolation zone of the Greenland ice sheet using fully polarimetric airborne X-, C-, L-, and P-band InSAR data. The results indicate that simple models are able to partially compensate the penetration bias and provide more accurate topographic information than the interferometric phase center measurements alone.

The penetration of microwave signals into snow and ice, especially in dry conditions, introduces a bias in digital elevation models generated by means of synthetic aperture radar (SAR) interferometry. This bias depends directly on the vertical backscattering distribution in the subsurface. At the same time, the sensitivity of interferometric SAR measurements on the vertical backscattering distribution provides the potential to derive information about the subsurface of glaciers and ice sheets from SAR data, which could support the assessment of their dynamics. The aim of this paper is to improve the interferometric modeling of the vertical backscattering distribution in order to support subsurface structure retrieval and penetration bias estimation. Vertical backscattering distributions are investigated at different frequencies and polarizations on two test sites in the percolation zone of Greenland using fully polarimetric X-, C-, Land nd P-band SAR data. The vertical backscattering distributions were reconstructed by means of SAR tomography and compared to different vertical structure models. The tomographic assessment indicated that the subsurface in the upper percolation zone is dominated by scattering layers at specific depths, while a more homogeneous scattering structure appears in the lower percolation zone. The performance of the evaluated structure models, namely an exponential function with a vertical shift, a Gaussian function and a Weibull function, was evaluated. The proposed models improve the representation of the data compared to existing models while the complexity is still low to enable potential model inversion approaches. The tomographic analysis and the model assessment is therefore a step forward towards subsurface structure information and penetration bias estimation from SAR data.

No abstract