Time Series Analysis of Global Daily Snow Cover Extents Derived from Medium Resolution Remote Sensing Data, Global SnowPack

Kurzfassung

Global snow cover is an important environmental parameter, as it influences hydrology, vegetation, radiation balance, and the living space of humans and animals. Snow is an essential source for freshwater in many regions of the world and at the same time, snow cover depends on precipitation and temperature during the snow season. As climate and weather varies, also the amount as well as the onset, duration, and offset of snow cover changes throughout the years. It is important to analyze this variability in order to identify possible trends, but also to predict the impact of the snow cover situation on local freshwater availability, floods, or the influences on vegetation. The presentation will focus on the production of daily, global snow cover extent datasets derived from medium resolution remote sensing data and their application in terms of time series analyses. Data sources include AVHRR, MODIS, and Sentinel 3. The presented products include the overall snow cover duration, early season snow cover duration, and late season snow cover duration. Together these products form the Global SnowPack: a set of snow cover parameters mage available on a global scale by DLR. The presentation will give a quick overview of how the processing steps are implemented before some examples are given on how the Global SnowPack datasets look like and what they can be used for. A long time series of daily snow cover datasets will be discussed in terms of possible trends, extreme events, and mean longterm conditions.