Multi-scale snow depth mapping in Interior Alaska using remote sensing

Annual seasonal snow cover is found in high latitude and high-altitude regions of the globe, with snowpack depth capable of being determined with automated devices, measuring sticks, or other similar instruments. Direct upscaling the sparse, field measured snow depth to regional scales (∼100 km 2 ) using remote sensing observations leads to large uncertainties due to the large difference in spatial scales. Linking field measurements with airborne remote sensing data to generate an intermediary snow product offers an opportunity to upscale the limited measurements to regional scales using large scale satellite observations. Here we developed an approach to generate an intermediary airborne snow depth map product at the local scale (1 km 2 ) first using end of season snow depth field measurements along limited transects between 2016 to 2022 and airborne hyperspectral and LiDAR data, and then further upscaled the airborne snow depth product to a regional scale (100 km 2 ) using satellite observations. Multiple regression approaches were applied in the upscaling. Snow depth patterns were identified in areas containing a presence or absence of thick canopy cover along with differences in snow depth between deciduous and evergreen forests. By generating airborne-based products as intermediate layers for regional scale upscaling, it allowed for reduced uncertainties when upscaling field measurements directly to a regional scale. This approach may be expanded beyond snow depth regional scale modeling to estimate snow water equivalent and snow density at regional scales by first generating intermediary local scale products from field data as inputs.