Abstract
Abstract. 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 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 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth 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 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 m for snow depth) when averaged to a 36 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.
Highlights
The snow depth or height of the snowpack is a key variable for both water resource management and avalanche forecasting in mountain regions
We found a good agreement between snow depth (HS) maps from high-resolution stereo satellite images with airplane laser-scanning HS maps over 138 km2 of mountainous terrain in California
Comparison of individual digital elevation models (DEMs) shows a growing positive bias with slope in Pléiades DEMs. This bias is of similar magnitude in both snow-on and snow-off Pléiades DEMs and cancels out in the HS map, leading to agreement between Pléiades and airplane laser-scanning HS for all slopes up to 60◦
Summary
The snow depth or height of the snowpack (symbol: HS; Fierz et al, 2009) is a key variable for both water resource management and avalanche forecasting in mountain regions. Determination of the spatial distribution of HS in complex terrain remains challenging due to its high spatial variability at horizontal scales below 100 m (Deems et al, 2006; Fassnacht and Deems, 2006). Current approaches to map HS are based on either sparse in situ measurements (Lopez-Moreno et al, 2011; Sturm et al, 2018), area-limited unmanned aircraft vehicle (UAV) campaigns (Bühler et al, 2016; De Michele et al, 2016; Harder et al, 2016; Redpath et al, 2018), terrestrial laser-scanning (Prokop et al, 2008; Fey et al, 2019) or costly airborne campaigns (Bühler et al, 2015; Dozier et al, 2016; Painter et al, 2016). Deschamps-Berger et al.: Snow depth mapping in mountainous terrain
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