| Summary: | Snow cover is an important variable for water availability, the radiation budget, glaciers, flora and fauna, and may cause natural disasters such as avalanches or floods. In many countries, snow is an important source of freshwater for reservoirs and the subsequent production of electricity. Climate change is affecting the global snow cover distribution, extent, and mass, influencing all the aforementioned parameters. It is therefore important to monitor the developments and changes to be able to detect possible trends and future impacts of changing snow cover on our environment. Remote Sensing offers an ideal data source to detect global snow cover with both high temporal and spatial resolution. Observations from medium resolution sensors such as AVHRR, MODIS, or Sentinel-3 provide time series of daily data, which can be processed and classified to calculate the snow cover extent on a global scale. Polar night, cloud cover, and complex terrain in mountain regions can cause data gaps and classification uncertainties. To overcome these problems, several techniques can be applied to the data in order to provide a consistent time series of daily snow cover information, which can further be processed to derive parameters such as snow cover duration per year or beginning/end of snow cover season. These parameters can be analysed to quantify the effects of climate change on snow cover on local, regional, and global scales. The presentation will outline the different steps in order to obtain snow cover information from remote sensing data and how these snow cover parameters can further be processed. Several examples will be presented for possible applications, and the opportunity of combining the results from remote sensing with those obtained by modelling/re-analysis will also be discussed.
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