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

• Published in: The Cryosphere
• Main Authors: Deschamps-Berger, César, Gascoin, Simon, Berthier, Etienne, Deems, Jeffrey, Gutmann, Ethan, Dehecq, Amaury, Shean, David, Dumont, Marie
• Other Authors: Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Université Grenoble Alpes (UGA), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS), National Snow and Ice Data Center (NSIDC), University of Colorado Boulder, National Center for Atmospheric Research Boulder (NCAR), Laboratory of Hydraulics, Hydrology and Glaciology, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology Zürich (ETH Zürich), Swiss Federal Institute for Forest, Snow and Landscape Research WSL, University of Washington Seattle, CNES Tosca, Programme National de Teledetection Spatiale (PNTS) : PNTS-2018-4, National Science Foundation (NSF) : 1852977, US Bureau of Reclamation Science and Technology Program, ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDV]Life Sciences [q-bio]; The Cryosphere
• Online Access: https://hal.inrae.fr/hal-02965637; https://hal.inrae.fr/hal-02965637/document; https://hal.inrae.fr/hal-02965637/file/2020_deschamps-berger_cryosphere.pdf; https://doi.org/10.5194/tc-14-2925-2020

International audience Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hy-drology 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 to-pographic 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 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.