A Bayesian classification model for sea ice roughness from scatterometer data

For sea ice in the Baltic Sea, surface scattering can be regarded as the do minant scattering mechanism at C-band. In this paper, a new statistical met hod is introduced for making statistical inferences about the underlying ic e surface roughness on the basis of one-dimensional (I-D) scatterometer dat a y. The central parameter in the hierarchical model applied in the context is a mixture parameter p, which indicates the degree of surface roughness in ice surface. Several questions related to the occurrence of different ic e classes on a transect can be solved with the aid of the posterior distrib ution [p/y]. An empirical approximation for the posterior distribution is c omputed by using Markov Chain Monte Carlo methodology. The efficiency of th e suggested approach is investigated by analyzing a C-band HH-polarization helicopter-borne HUTSCAT scatterometer data. The results provided by the st atistical model show good agreement with a video-based ice type classificat ion.