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Discussion papers
https://doi.org/10.5194/tc-2020-15
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-15
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Feb 2020

Submitted as: research article | 04 Feb 2020

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This preprint is currently under review for the journal TC.

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

César Deschamps-Berger1,2, Simon Gascoin1, Etienne Berthier3, Jeffrey Deems4, Ethan Gutmann5, Amaury Dehecq6,7, David Shean8, and Marie Dumont2 César Deschamps-Berger et al.
  • 1Centre d’Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRA/IRD/UPS, 31401 Toulouse, France
  • 2Université Grenoble Alpes, Université de Toulouse, Météo-France, Grenoble, France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France
  • 3Centre National de la Recherche Scientifique (CNRS-LEGOS), 31400 Toulouse, France
  • 4National Snow and Ice Data Center, Boulder, CO, USA
  • 5Research Applications Lab, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
  • 6Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 7Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 8University of Washington, Dept. of Civil and Environmental Engineering, Seattle, WA, USA

Abstract. An accurate knowledge of snow depth distribution in mountain catchments is critical for applications in hydrology and ecology. A recent new method was proposed to map the snow depth at meter-scale resolution from very-high resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.50 m. However, the validation was mainly done using probe measurements which sampled a limited fraction of the topographic and snow depth variability. We deepen this evaluation using accurate maps of the snow depth derived from ASO airborne lidar measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 137 km2 on a 3 m grid with a positive bias for Pléiades snow depth of 0.08 m, a root-mean-square error of 0.80 m and a normalized median absolute deviation 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. The random error on snow depth can be reduced by a factor two (up to approximately 0.40 m) when the snow depth map is spatially averaged to a ~ 20 m grid. The random error at the pixel level is lower on snow-free areas than on snow-covered areas, but errors on both terrain type converge at coarser resolutions, which is important for further applications of the method in areas without snow depth reference data. We conclude that satellite photogrammetry stands out as an efficient method to estimate the spatial distribution of snow depth in high mountain catchments.

César Deschamps-Berger et al.

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César Deschamps-Berger et al.

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