PICO
Snow constitutes a freshwater resource for over a billion people worldwide. A high percentage of this water resource mainly comes from seasonal snow. The ongoing warming poses a significant risk to snow water storages, potentially leading to a drastic reduction in water supply and causing adverse effects on the ecosystems.
Therefore, understanding seasonal snow dynamics, possible changes, and implications have become crucial for water resources management.
Remote sensing technology plays a crucial role in monitoring snow properties and their hydrological implications across spatial and temporal scales, allowing for a better understanding of snow dynamics (e.g., the interaction of snow with small-scale, quick snow changes within a day, rain on snow events, snow-vegetation interaction).
This session focuses on studies linking the use of remote sensing of seasonal snow to hydrological applications to: (i) quantify snow characteristics (e.g., SWE, snow grain size, albedo, pollution load, snow cover area, snow depth and snow density), (ii) understand and model snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (iii) assess snow hydrological impacts and snow environmental effects. Works including technique and data from different technologies (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications) across spatial (from the plot to the global) and temporal (from instantaneous to multiyear) scales are welcome.
Therefore, understanding seasonal snow dynamics, possible changes, and implications have become crucial for water resources management.
Remote sensing technology plays a crucial role in monitoring snow properties and their hydrological implications across spatial and temporal scales, allowing for a better understanding of snow dynamics (e.g., the interaction of snow with small-scale, quick snow changes within a day, rain on snow events, snow-vegetation interaction).
This session focuses on studies linking the use of remote sensing of seasonal snow to hydrological applications to: (i) quantify snow characteristics (e.g., SWE, snow grain size, albedo, pollution load, snow cover area, snow depth and snow density), (ii) understand and model snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (iii) assess snow hydrological impacts and snow environmental effects. Works including technique and data from different technologies (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications) across spatial (from the plot to the global) and temporal (from instantaneous to multiyear) scales are welcome.