Keskinen, ZacharyHendrikx, JordyEckerstorfer, Markus2022-12-062022-12-062022-07Keskinen, Z., Hendrikx, J., Eckerstorfer, M., & Birkeland, K. (2022). Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate. Cold Regions Science and Technology, 199, 103558.0165-232Xhttps://scholarworks.montana.edu/handle/1/17443© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Snow avalanches endanger lives and infrastructure in mountainous regions worldwide. Consistent and accurate datasets of avalanche events are critical for improving hazard forecasting and understanding the spatial and temporal patterns of avalanche activity. Remote sensing-based identification of avalanche debris allow for the acquisition of continuous and spatially consistent avalanches datasets. This study utilizes expert manual interpretations of Sentinel-1 synthetic aperture radar (SAR) satellite backscatter images to identify avalanche debris and compares those detections against historical field records of observed avalanches in the transitional snow climates of Wyoming and Utah, USA. We explore and quantify the ability of an expert using Sentinel-1 (a SAR satellite) images to detect avalanche debris on a dataset comprised exclusively of dry slab avalanches. This research utilized four avalanche cycles with 258 field reported avalanches. Due to individual avalanches appearing in multiple overlapping Sentinel-1 images this resulted in 506 potential detections of avalanches in our SAR images, representing the possibility of multiple detections of a single avalanche event in different images. The overall probability of detection (POD) for avalanches large enough to destroy trees or bury a car (i.e., ≥D3 on the destructive size scale) was 65%. There was a significant variance in the POD among the 13 individual SAR image pairs considered (15–86%). Additionally, this study investigated the connection between successful avalanche detections and SAR-specific, topographic, and avalanche type variables. The most correlated variables with higher detection rates were avalanche path lengths, destructive size of the avalanche, incidence angles for the incoming microwaves, average path slope angle, and elapsed time between the avalanche and a Sentinel-1 satellite image acquisition. This study provides a quantification of the controlling variables in the likelihood of detecting avalanches using Sentinel-1 backscatter temporal change detection techniques, as specifically applied to a transitional snow climate.en-UScc-by-nc-ndhttps://creativecommons.org/licenses/by-nc-nd/4.0/remote sensingsnow avalanchesterrainsynthetic aperture radarArticle