An assessment of highly variable prairie snow from multiple sensing platforms

A NASA SnowEx field campaign focused on seasonal prairie snow was conducted in central Montana, USA during the winter of 2020-2021. The 1 km2 study site was located within an agricultural research station (47, -110) comprised of 12 different crop types with various stubble heights, aligned in rows as close as 0.25 m. The depth and spatial distribution of the sites prairie snowpack was highly variable, driven by the dominant controls of wind (seasonal mean velocity >4 ms-1) and stubble height. We present a comparative analysis of the sites snowpack depth and spatial variability using four different snow measurement techniques. The techniques range from traditional methods to state-of-the-science: manual depth transects, L-Band UAVSAR mounted on an airplane, and UAV-based LiDAR & Structure from Motion (SfM) photogrammetric reconstructions. Results show that the UAVSAR instrument accurately captured the spatial variability of snow cover and depth across the different crop types and row distances, and are well aligned spatially with the high-resolution LiDAR data. Similarly, UAV-based reconstructions of snow depth from LiDAR and SfM show a high degree of correlation. Snow pit measurements of snow water equivalent (SWE) are used to estimate SWE for the entire study area, and then related back to the UAVSAR data. Combined, these data and analyses are intended to support algorithm development and modeling to estimate seasonal snow and SWE in prairie environments for current and future SAR satellite missions.