In cryospheric studies, the most critical variable is estimating the beginning and evolution of the seasonal snow accumulation and its melting process. We propose and evaluate a new method for identifying seasonal snow cover phase changes in the Argentinean Andes using time series of Sentinel-1 synthetic aperture radar data available in the Google Earth Engine platform. We used meteorological and optical Sentinel-2 data to validate snow presence. First, we investigated seasonal snow cover dynamics in different regions of interest (ROIs). We identified three land surface cover periods: bare soil, dry snow, and melting snow. This finding is significant because other studies show that bare soil and dry snow have similar backscattering responses in C-band. Our methodology uses time series derivatives and their positive and negative anomalies. Finally, we compare our results with those obtained with a fixed threshold change detection approach. Our method was able to detect the phase change between bare soil and dry snow period in 75% of the ROIs, while a fixed threshold of only detects it in 42% of the cases. Furthermore, the derivative method also detects in 92% of the ROIs time series the beginning of the melting period, showing that it is a promising methodology for operative systems.