Day 4

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Paper title Monitoring of the seasonal snowpack with satellite photogrammetry
Authors
  1. César Deschamps-Berger Instituto Pirenaico de Ecologia Speaker
  2. Simon Gascoin CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS
  3. Marie Dumont CNRM (CNRS/Météo-France) / Centre d'Etudes de la Neige
  4. Etienne Berthier LEGOS Toulouse
  5. Bartek Luks Institute of Geophysics, Polish Academy of Sciences
  6. Juan Ignacio Lopez Moreno Instituto Pirenaico de Ecologia
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text The seasonal snow cover in mountains is crucial for ecosystems and human activities. Developing methods to map snow depth at high resolution ("< 10 m") is an active field of snow studies as snow depth is a key variable for water ressource and avalanche risk assessment. Most methods rely on close range remote sensing, combining lidar or photogrammetry with an airplane or a drone. However, drone acquisitions are limited to small areas ("< 10 km²") and airborne campaigns are logistically difficult to set up in many mountains of the world. Satellite photogrammetry is an innovative method for monitoring the seasonal snowpack in mountains and could help address the challenge of estimating the distribution of snow in any place of the world. Accurate snow depth maps at high spatial resolution ("~ 3 m") are calculated by differencing digital elevation models with and without snow derived from satellite stereoscopic images.
Here we present a collection of snow depth maps calculated from 50 cm Pléiades stereoscopic images in the central Andes, the Alps, the Pyrenees, the Sierra Nevada (USA) and Svalbard. The comparison with a reference snow-depth map measured with airborne lidar in the Sierra Nevada, provides a robust estimation of the Pléiades snow depth error. At the 3 m pixel scale, the standard error is about 0.7 m. The error decreases to 0.3 m when the snow-depth maps are averaged over areas greater than "10^3 m²". Specific challenge arose in some sites due to the lack of snow free terrain or due to artefacts inherent to satellite images. However, Pléiades snow depth maps are sufficiently accurate to allow the observation of snow redistribution patterns due to wind transport and avalanche, or the precise determination of the snow volume in a "100 km²" catchment. Assimilated in a distributed snowpack model, Pléiades snow depth amps improve the modeled spatial variability of the snow depth and compensate for lacking processes in the model or bias in the meteorological forcings. The available collection of Pléiades snow depth maps provides the opportunity to characterize with a consistent method the snow cover in an unprecedented variety of sites, such as the arctic, alpine mountains and subtropical regions.