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Apport de la photogrammétrie satellite pour la modélisation du manteau neigeux

Abstract : Mountain snowpack is a major resource for ecosystems and human activities. It supplies water for crop irrigation, human consumption, hydropower industries and the tourism sector. It is also a cause of damage in avalanche prone areas. The monitoring and study of mountain snowpack usually rely on field measurement networks, close range remote sensing and modeling. Recent improvements in satellite photogrammetry provide an alternative to measure the high spatial variability of the snowpack, which cannot be sampled by automatic networks. The results presented here, contribute to improve the mapping of snow-depth in mountains with satellite photogrammetry, a key variable for hydrology and risk assessment. Snow-depth maps from pairs and triplets of stereo images of the Pléiades satellite are calculated at several sites. The comparison with a reference snow-depth map measured with airborne lidar in California (USA), provides a robust estimation of the satellite products 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 103 m2. With this accuracy, Pléiades snow-depth maps allow the observation of the processes modeling mountain snowpack (wind transport, avalanche), the measurement of the snow volume over a 100 km2 area and the description of the spatial variability of the snowpack. The assimilation of such satellite snow-depth maps in the SAFRAN-Crocus snowpack model, resulted in promising outcomes for a mountainous catchment in the Pyrenees. A particle filter is used on a regular grid with 250 m spacing over five winters with one assimilation date per winter, near peak accumulation. The assimilation corrects an underestimation of the precipitation in the meteorological forcings. It also introduces spatial variability otherwise lacking in the forcings and the processes modeled. This innovative use of satellite products and complex spatial modeling, could help address the challenge of estimating snow distribution in the world's mountains.
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Submitted on : Friday, September 3, 2021 - 2:30:16 PM
Last modification on : Friday, December 2, 2022 - 10:03:34 AM
Long-term archiving on: : Saturday, December 4, 2021 - 7:20:13 PM


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  • HAL Id : tel-03334086, version 1


César Deschamps-Berger. Apport de la photogrammétrie satellite pour la modélisation du manteau neigeux. Hydrologie. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30044⟩. ⟨tel-03334086⟩



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