Performance of manual and automatic detection of dry snow avalanches in Sentinel-1 SAR images

Radar satellite-borne snow avalanche detection has rapidly grown into an important method for monitoring of avalanche activity over large spatial and long temporal scales. With increased application of Sentinel-1 data for avalanche detection, the need for improved performance evaluation arises. In this study, we make use of a unique dataset of field-based avalanche observations from eight adjacent avalanche paths at lake Tyin in central Norway. The dataset is a complete record of 318 dry slab avalanches that released during the winters 2016-2020, with information on release timing and extent. The dataset thus allows for detailed evaluation of the performance of automatic and manual avalanche detection in Sentinel-1 images, were both techniques make use of relative temporal backscatter intensity increase in case of an avalanche. Both automatic and manual detection underperform compared to previous studies with a probability of detection (POD) of 5.9% (false alarm rate (FAR) of 5.9%) and 11.3% (FAR of 19.27%) respectively. From the low relative backscatter intensity contrast of fieldobserved dry slab avalanches, it becomes evident that a higher backscatter contrast between avalanches and surrounding is needed for detectability in Sentinel-1 images. Neither the lag time between avalanche release and detection in a Sentinel-1 image, nor local incidence angle can explain the low POD's. Moreover, an analysis of meteorological conditions prior a during avalanche release and/or detection can explain the low POD's, given that differing snow conditions influence radar backscatter intensity. Finally, we cannot rule out that the small dataset of field-observed avalanches and the one-directional slope aspect of the study area influence our results, however, we believe that a physical limit of detectability of dry snow avalanches in C-band radar satellite data is reached.