Using SAR satellite data time series for regional glacier mapping

• Published in: The Cryosphere
• Main Authors: Winsvold, Solveig Havstad, Kääb, Andreas, Nuth, Christopher, Andreassen, Liss Marie, Van Pelt, Ward J.J., Schellenberger, Thomas
• Format: Article in Journal/Newspaper
• Language: English
• Published: National Snow and Ice Data Center 2018
• Subjects: Norway; Svalbard; glacier; The Cryosphere
• Online Access: http://hdl.handle.net/10852/61549; http://urn.nb.no/URN:NBN:no-64150

With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series data.