Detecting seasonal snow transitions in SAR time series with Horizontal Visibility Graphs

Abstract

Seasonal snow studies in the Andes face major challenges due to limited ground access and the region’s complex mountain topography. Despite the crucial role of snow in hydrological and climatic processes, research on seasonal snow dynamics in Latin America remains scarce, particularly regarding the use of advanced remote sensing techniques. Synthetic Aperture Radar (SAR) image time series offer a powerful tool to detect the onset of snowmelt processes in such data-scarce environments. However, the potential of SAR to discriminate between bare soil and fresh snow remains largely unexplored, representing an important gap in current remote sensing methodologies. In this study, we analyzed SAR time series via Horizontal Visibility Graphs (HVG) with modularity-based community detection. The proposed method detected a previously unreported bare soil transition (summer-autumn) that conventional threshold-based techniques missed, as shown by SAVI/NMDI validation. These findings demonstrate that community extraction from HVG provides a robust and insightful framework for analyzing temporal snow dynamics. This approach is critical for water resource management in the Patagonian Andes, where ground monitoring is limited, and snowmelt timing directly impacts regional runoff, hydropower generation, and flood risk mitigation.