Enhancing Understanding of Snow Dynamics Using SAR Interferometric Observables: A Case Study in Sodankyla Forest

Snow, a crucial component of the cryosphere, significantly impacts global climate monitoring and has an important role in freshwater supply, hydropower energy and tourism. Advances in remote sensing technologies, particularly Interferometric Synthetic Aperture Radar (InSAR), enhance our understanding of snow processes. This study investigates the potential use of interferometric observables (phase, coherence, and phase closure) from a ground-based L-band SAR sensor to analyze snow dynamics. Time series data expanding one winter and one summer season are assessed with in-situ snow depth, snow water equivalent and air temperature observations. We found that by exploiting all three interferometric observables the identification of the start and the end of each snow season (wet snow, dry snow, no snow) can be performed more accurately. This study highlights the potential of L-band interferometric observables that are relevant for future L-band SAR missions.