| Summary: | This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric synthetic aperture radar acquisitions is firstly detailed. Large-scale mosaics of radar backscatter and volume correlation factor, derived from quicklook images of the interferometric coherence, represent the starting point for applying an unsupervised classification method based on the c-means fuzzy clustering algorithm. The data was acquired during winter 2010/2011. A partition of four different snow facies was chosen and interpreted using reference melt data, snow density, and in situ measurements. The variations in the stratification and micro-structure of firn, such as the variations of density with depth and the presence of percolation features, are identified as relevant parameters for explaining the significant differences in the observed interferometric signatures among different snow facies. Moreover, a statistical analysis of backscatter and volume correlation factor provided useful parameters for characterizing the snow facies behavior and analyzing their dependency on the acquisition geometry. Finally, knowing the location and characterization of the different snow facies, the two-way X-band penetration depth over the whole Ice Sheet was estimated. The obtained mean values vary from 2.3 m for the outer snow facies up to 4.18 m for the inner one. The presented approach represents a starting point for a long-term monitoring of ice sheet dynamics, by acquiring time-series, and is of high relevance for the design of future SAR missions as well.
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