Snow is an important component of the global hydrologic cycle, with significant implications for water resources and natural hazards. Traditionally, snow water equivalent (SWE) has been estimated…

The National Aeronautics and Space Administration (NASA) Shared Services Center awarded a $423,688.00 grant to the University of Washington (UW) for research on snow depth and snow water equivalent (SWE) estimation using commercial optical satellite imagery. The research aims to develop a capability to assess snow depth and SWE on a regional scale, addressing the limitations of existing methods that rely on sparse in-situ measurements or low-resolution remote sensing. UW will utilize an automated processing workflow to generate time series of high-resolution digital elevation models from stereo satellite imagery, building on previous work demonstrating the viability of this approach for measuring changes in glacier and ice sheet elevations. The grant does not have a set-aside designation.

Snow is an important component of the global hydrologic cycle, with significant implications for water resources and natural hazards. Traditionally, snow water equivalent (SWE) has been estimated for watersheds and mountain ranges using sparse in situ measurements (e.g., snotel) and/or highly parameterized models calibrated with low-resolution remote sensing products (e.g., modis fsca). In recent years, repeat airborne lidar observations by the NASA airborne snow observatory (ASO) offer detailed maps of snow depth with high accuracy and spatial resolution, but logistical costs are high, and coverage is limited to individual watersheds. Despite these existing capabilities, IT remains challenging to accurately assess snow depth and SWE on a regional scale. To address this issue, we will use ~30-50 CM commercial optical stereo satellite imagery (digitalglobe worldview/geoeye constellation) to estimate snow depth and SWE on a regional scale. We will generate time series of CO-registered, 2-m digital elevation models (dems) using a mature, automated, open-source processing workflow (NASA ames stereo pipeline, running on the NASA pleiades supercomputer). Previous work demonstrated that this approach can be used to measure glacier and ICE sheet elevation change with