Conference Agenda
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Daily Overview |
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📌Poster Session and Networking Aperitivo 🍷 Location: Lower Lobby | |
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Multi-Stage Coherence-Polarization Fusion Modeling for Snow Depth and Snow Water Equivalent Estimation from Sentinel-1 Dual-Pol Data Indian Institute of Technology Bombay Accurate retrieval of snow depth (SD) and snow water equivalent (SWE) is critical for understanding seasonal snow dynamics, meltwater contribution, and hydrological variability across mountainous terrain. While several studies have employed quad-polarized SAR systems for snow parameter retrieval, the application of free source dual-polarized C-band Sentinel-1 SAR for quantitative SD and SWE estimation remains limited. This study introduces a multi-level Polarimetric SAR-based framework that utilises Sentinel-1 dual-polarization observations for snow parameter retrieval over the Dhundi–Manali region of the western Himalaya, with cross-study using SNOTEL Colorado and Salt Lake regions, USA and SnowEx datasets. The framework was developed in three progressive stages: (1) a SAR GRD-based model employing VH/VV backscatter ratios between snow and reference (non-snow) acquisitions to capture surface scattering variations; (2) a polarimetric C-matrix decomposition model, designed to quantify dielectric and structural anisotropy of the snowpack; and (3) a coherence-assisted PolInSAR extension, utilizing multi-temporal Sentinel-1 acquisitions to evaluate temporal coherence decay associated with snow metamorphism. The integration of coherence with polarimetric parameters enhances the sensitivity of Sentinel-1 to vertical snow structure and wetness evolution. Results indicate that the cross-polarized (VH) coherence exhibits a strong negative correlation with in-situ snow depth (r ≈ 0.65) under dry to moderately wet snow conditions. The empirical models achieved an RMSE of approximately 10–15 cm, demonstrating good retrieval accuracy across varying topography and snow types. Application of the same empirical relationship over the other alpine sites gave better model transferability and comparable sensitivity trends across contrasting climatic conditions. Overall, the study provides demonstrations of applying PolInSAR principles to dual-polarized Sentinel-1 SAR data for quantitative estimation of SD and SWE. The proposed methodology establishes a scalable, all-weather, and temporally consistent approach for snowpack characterization at 50–100 m spatial resolution, offering substantial potential for regional-to-continental cryosphere monitoring. | |
