Abstract ID: 28.7458 | Accepted as Poster | Poster | 2025-02-28 12:45 - 14:15 | Ágnes‐Heller‐Haus/Small Lecture Room

Information on snow cover and the properties of snow (wet / dry) plays an important role in mountain areas, for example within snow hydrology, meteorology, management of water resources, flood protection and avalanche warnings. This also applies to glaciated regions and being able to monitor the snow state on a glacier is potentially beneficial in better modelling and understanding other properties of the glacier. The state of snow is a highly variable parameter that requires frequent observations while also covering large areas of the Earth. Satellites allow for these requirements to be fulfilled. We will be presenting the development of a wet snow classification algorithm, suitable for mountain areas. The algorithm is currently used for the SAR Wet Snow product, a part of the Pan-European High-Resolution Snow & Ice Monitoring of the Copernicus Land Monitoring Service. The product detects wet snow from snow-free / patchy / dry snow at 60m by 60m resolution using Sentinel-1 C-band SAR, in near-real-time. The snow classification algorithm is based on change detection, using the ratio of the backscatter coefficient of wet snow versus the coefficient during snow-free or dry snow conditions. This is because the latter conditions are transparent at C-band in contrast to the backscatter properties of wet snow. Using this information and applying a threshold value for the ratio we are able to detect wet snow. Due to the properties of synthetic aperture radar instruments this method is able to function independent of the cloud cover and time of day. There are also some limitations of the algorithm. Due to the backscatter properties of forested areas, urban areas and bodies of water, change detection will not work reliably for the detection of wet snow. The poster will present the wet snow product over time, showing the melting season and highlighting the variability of snow properties. The importance of monitoring snow conditions in mountain areas and glaciers will also be present. The change detection algorithm will be summarized, including its strengths and limitations.

Authors: Andrea Scolamiero, Markus Hetzenecker, Gabriele Schwaizer, Thomas Nagler