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Large-scale snow depth mapping from moderate resolution satellite imagery
Snow water storage is a key variable of the water cycle, yet remains difficult to observe. Snow depth estimations rely on point field measurements or costly, airborne campaigns. This project attempts to harness moderate resolution satellite imagery to map snow depth consistently at large scales.
The snow depth or height of the snowpack is an essential climate variable which impacts water resource management and avalanche forecasting in many of the world’s mountainous regions. Yet, quantifying snow depth at large scales in snow-rich areas remains challenging due to its high spatial variability and the sparse amount of available observations. Those observations are generally based on in-situ measurements, and sometimes complemented with dedicated and costly airborne campaigns (unmanned aircraft vehicles, terrestrial laser scanning, ..). Such high-resolution observations only exist for a few regions and were generally acquired after the 2010s.
The snow depth or height of the snowpack is an essential climate variable which impacts water resource management and avalanche forecasting in many of the world’s mountainous regions. Yet, quantifying snow depth at large scales in snow-rich areas remains challenging due to its high spatial variability and the sparse amount of available observations. Those observations are generally based on in-situ measurements, and sometimes complemented with dedicated and costly airborne campaigns (unmanned aircraft vehicles, terrestrial laser scanning, ..). Such high-resolution observations only exist for a few regions and were generally acquired after the 2010s.
The goal of this thesis is to assess snow depth variations at large spatial scales by using digital elevation models (DEMs, i.e. numerical maps of Earth’s surface elevation). These DEMs were generated by stereo-photogrammetry after the recent opening of two decades of global acquisitions from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), onboard NASA’s satellite Terra. The study will focus on specific study areas where independent, high-resolution data exists for validation (in-situ, high-resolution airborne campaigns). For this, a possible collaboration with the University of Northern British Columbia (Brian Menounos) is envisioned for exploiting seasonal LiDAR mapping of snow depth in the Canadian Rockies. The local application could be extended to entire regions (e.g., the Alps) using large-scale validation datasets such as satellite laser altimetry (ICESat, ICESat-2). The results will be of high relevance for further hydrological analyses.
The goal of this thesis is to assess snow depth variations at large spatial scales by using digital elevation models (DEMs, i.e. numerical maps of Earth’s surface elevation). These DEMs were generated by stereo-photogrammetry after the recent opening of two decades of global acquisitions from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), onboard NASA’s satellite Terra. The study will focus on specific study areas where independent, high-resolution data exists for validation (in-situ, high-resolution airborne campaigns). For this, a possible collaboration with the University of Northern British Columbia (Brian Menounos) is envisioned for exploiting seasonal LiDAR mapping of snow depth in the Canadian Rockies. The local application could be extended to entire regions (e.g., the Alps) using large-scale validation datasets such as satellite laser altimetry (ICESat, ICESat-2). The results will be of high relevance for further hydrological analyses.
For further information please contact Prof. Daniel Farinotti (daniel.farinotti@ethz.ch) or Romain Hugonnet (rhugonnet@ethz.ch).
For further information please contact Prof. Daniel Farinotti (daniel.farinotti@ethz.ch) or Romain Hugonnet (rhugonnet@ethz.ch).