Monitoring of Wet Snow and Accumulations at High Alpine Glaciers Using Radar Technologies

Abstract

ABSTRACT:Conventional studies to assess the annual mass balance for glaciers rely on single point observations in combination with model and interpolation approaches. Just recently, airborne and spaceborne data is used to support such mass balance determinations. Here, we present an approach to map temporal changes of the snow cover in glaciated regions of Tyrol, Austria, using SAR-based satellite data. Two dual-polarized SAR images are acquired on 22 and 24 September 2014. As X and C-band reveal different backscattering properties of snow, both TerraSAR-X and RADARSAT-2 images are analysed and compared to ground truth data. Through application of filter functions and processing steps containing a Kennaugh decomposition, ortho-rectification, radiometric enhancement and normalization, we were able to distinguish between dry and wet parts of the snow surface. The analyses reveal that the wet-snow can be unambiguously classified by applying a threshold of -11 dB. Bare ice at the surface or a dry snowpack does not appear in radar data with such low backscatter values. From the temporal shift of wet-snow, a discrimination of accumulation areas on glaciers is possible for specific observation dates. Such data can reveal a periodic monitoring of glaciers with high spatial coverage independent from weather or glacier conditions.

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

“…The speckle noise in SAR images affects all SAR-based snow cover detection algorithms, especially the backscattering-based approaches. The available options for filtering algorithms to overcome this problem include Frost filter [30,126,137,142,186,187,195], refined Lee filter [96,132,163,205,206], median filter [102,103,131], low pass filter [135,207], multichannel intensity filter [140], binary partition tree [167], De Grandi filter [208], multi-scale multilooking [209], and Kuan filter [136]. Some studies attempted to compare the ability of different filters.…”

Crevasse density, orientation and temporal variability at Narsap Sermia, Greenland

“…Finally, the GCD requires a surface expression of crevasses in optical imagery, and will fail to identify crevasses entirely buried by snow (which can be identified using other methods; e.g. Eder and others, 2008; Thompson and others, 2020). The use of SAR data could enable crevasse detection in cases where it is not possible in optical data, but we limit our use of the GCD to optical data in this study.…”

TerraSAR-X Time Series Fill a Gap in Spaceborne Snowmelt Monitoring of Small Arctic Catchments—A Case Study on Qikiqtaruk (Herschel Island), Canada

“…The reduced backscatter signal of melting snow, as well as the increased backscatter of frozen ice layers, is the basis for a threshold segmentation that efficiently differentiates between wet snow or ice layers and dry snow or no snow. While this method was originally developed for C-Band of ERS-1 data and was further improved and adapted for ERS-2, RADARSAT-1, ENVISAT ASAR and Sentinel-1 data [40,68,69], it was also applied for X-Band data from TSX and COSMOSkyMed data [38,70,71]. The backscatter intensity of images acquired during the spring show notable differences between acquisitions caused by drifting sea ice and snowmelt that reduce the quality of co-registration when using cross-correlation.…”