Machine learning to infer surface mass balance of glaciers by means of snow cover, in-situ measurements and volume changes from earth observation

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Changes in glacier area, elevation and mass are major indicators for climate change and are identified as “Essential Climate Variables” by the World Meteorological Organization. The MASSIVE project will focus on the development of machine learning methods for snow cover detection over glacierized areas and glacier mass balance estimation, exploiting different sources of satellite data. These will include high and medium resolution multi-spectral images (e.g. Sentinel-2 and Sentinel-3) as well as synthetic aperture radar data (e.g. Sentinel-1). The methodology will be designed and tested over in-situ monitored glaciers in Norway, Svalbard and European Alps. Then, the transferability to glacierized regions with less ground data available will be tested. The project will allow to build and automatically update a consistent time-series of glacier surface mass balance and area change. These are highly valuable data for the hydropower industry, governmental agencies and the research community, e.g. to improve runoff forecast for enhanced water management (e.g. drinking water, hydropower, agriculture) and to increase the knowledge about glaciers as climate variable, their fading in a warming climate and their contribution to global sea-level rise.

Contact person: Mattia Callegari

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