Global Snow Cover Mapping Using a Multi-Temporal Multi-Sensor Approach

Snow cover maps based on satellite data from optical and passive microwave radiation sensors have different properties. In this paper we describe a method for creating a merged multi-temporal snow extent product using a hidden Markov model. The Viterbi algorithm is used to find the most likely sequence of snow states on the ground, given a time series of snow products from the two satellite sensors. The resulting snow extent product provides snow cover estimates during nighttime and cloud cover, while being robust with respect to thin snow cover and wet surfaces. An initial validation of the algorithm is performed, resulting in an overall accuracy of the product of 92.4%. We resulting snow cover product is robust, with improved accuracies related to wet snow surfaces that often is a challenge for snow extent products based on PMR.