Characterization of Seasonal Snow Covered Surfaces by Sentinel 1 Time Series Anomalies

Seasonal snow is one of the most dynamic components of the cryosphere. The structure and composition of snowpacks is complex and highly variable, both in space and also in time. This study evaluates a new method for identifying phase changes of seasonal snow cover in the Argentinean Andes using Sentinel-1 Synthetic Aperture Radar (SAR) data available in Google Earth Engine (GEE) through time series derivatives and positive and negative anomalies. The results were compared with an approach based on fixed threshold time series phase change detection.