Snow is a valuable freshwater reservoir throughout the globe. In regions where summer precipitation is limited and infrequent, the amount of water in the snowpack is crucial as these regions rely on annual spring melt for their water supply (both potable and agricultural). Moreover, large seasonal snow packs as well as other hydrologic characteristics (including SWE, soil moisture, ground water, frozen ground, etc.) often cause extraordinary flood hazards or may impact recreation and tourism during years with limited winter precipitation. In situ observations provide point-scale estimates of snow; however, these observations are sparse and are often not suitable for global-, regional-, or even watershed-scale investigations. Terrain, vegetation, and micrometeorological conditions often interact to cause large spatial variations in snow pack properties at sub-km scales, due to differential accumulation, redistribution, and melt, complicating the estimate of snow in mountainous regions. Therefore, satellite remote sensing and land surface modeling offer a means to address both the spatial and temporal scales of the sampling problem. Existing and novel remote sensing techniques have been developed to directly estimate snow properties. Land surface and hydrologic models have shown the use of snow properties, such as snow water equivalent, as important prognostic and diagnostic variables through modeling efforts. This session invites research on existing and novel methods for remote sensing of snow properties, modeling efforts to estimate snow properties, data assimilation techniques of using remote sensing observations within a modeling construct, and combinations of these frameworks to improve snow estimation capabilities.