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

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