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Open AccessArticle

Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

1
Department of Earth and Environmental Sciences, Munich University, 80539 Munich, Germany
2
Geodesy and Glaciology, Bavarian Academy of Sciences and Humanities, 80539 Munich, Germany
3
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Land Surface Applications, 82234 Oberpfaffenhofen, Germany
4
Department of Geoinformatics, University of Applied Sciences, 80335 Munich, Germany
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(2), 69; https://doi.org/10.3390/geosciences9020069
Received: 10 December 2018 / Revised: 14 January 2019 / Accepted: 22 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations. View Full-Text
Keywords: SAR; transient snowline; annual AAR; mass balance; Rofental glaciers SAR; transient snowline; annual AAR; mass balance; Rofental glaciers
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MDPI and ACS Style

Heilig, A.; Wendleder, A.; Schmitt, A.; Mayer, C. Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria. Geosciences 2019, 9, 69.

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