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

• Main Authors: C. Deschamps-Berger, S. Gascoin, E. Berthier, J. Deems, E. Gutmann, A. Dehecq, D. Shean, M. Dumont
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-09-01
• Series: The Cryosphere
• Online Access: https://tc.copernicus.org/articles/14/2925/2020/tc-14-2925-2020.pdf
• ISSN: 1994-0416; 1994-0424

<p>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., Pléiades) with an accuracy close to 0.5&thinsp;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&thinsp;km<span class="inline-formula">²</span> on a 3&thinsp;m grid, with a positive bias for a Pléiades snow depth of 0.08&thinsp;m, a root mean square error of 0.80&thinsp;m and a normalized median absolute deviation (NMAD) of 0.69&thinsp;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 0.40&thinsp;m for snow depth) when averaged to a 36&thinsp;m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.</p>