Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pleiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km(2) on a 3m grid, with a positive bias for a Pleiades snow depth of 0.08 m, a root mean square error of 0.80m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40m for snow depth) when averaged to a 36m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.