Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps

Seasonal snow cover is a valuable indicator of climatic variations due to its sensitivity to temperature and precipitation. Complementary to ground-based station data, satellite time series provide large-scale spatial capabilities. The primary disadvantage of this technique, however, is the relative brevity of records. Only AVHRR offers the opportunity to analyze more than 25 years of medium-resolution satellite imagery on a daily basis. AVHRR thereby holds a great potential to detect, map and quantify long-term environmental changes. However, to serve this purpose though, adequate algorithms and careful validation are of major significance. we describe and extensively validate snow cover retrieval from historical 1-km AVHRR data using a stable snow detection algorithm, which allows consistent snow sampling across all AVHRR sensors. As a new asset, a pixel-wise probability map based on logistic regression is provided for each snow mask. The spatial and seasonal validation includes a comparison to MOD10A1 and webcam imagery. In addition, the influence of acquisition geometry and the sensor-to-sensor consistency have been investigated using LANDSAT TM data and a snow climatology based on long-term station data. clude that the snow detection algorithm tested allows for a 1-km snow extent climatology to be generated from the 25-year full-resolution AVHRR data archived at the University of Bern with favorable accuracy and stability. Given the importance of mountainous regions for climate change studies, this satellite-based data set could become an important tool for assessing environmental changes in the European Alps.