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A novel sensor fusion algorithm for retrieving the freeze/thaw (f/t) state from scatterometer data is presented: It is based on a probabilistic model, which is a variant of the Hidden Markov model, and it computes the probability that the landscape is frozen, thawed, or thawing for each day. By combining Ku- and C-band scatterometer data, the distinct backscattering properties of snow, soil, and vegetation at the two radar bands are exploited. The parameters that are necessary for inferring the f/t state are estimated in an unsupervised fashion, i.e., no training data are required. Comparison with model and in situ temperature data in a test area in Siberia/northern China indicates that the approach yields promising results (typical accuracies exceeding 90%); difficulties are encountered over bare rock and areas where large fluctuations in soil moisture are common. These limitations turn out to be closely linked to the inherent assumptions of the probabilistic model.