A sea ice mapping algorithm for SeaWinds is developed that incorporates statistical and spatial a priori information in a modified maximum a posteriori (MAP) framework. Spatial a priori data are incorporated in the loss terms of a Bayes risk formulation. Conditional distributions and priors for sea ice and ocean statistics are represented as empirical histograms that are forced to conform to a set of expected histograms via principal component filtering. Tuning parameters for the algorithm allow adjustments in the algorithm's performance. Results of the algorithm exhibit high correlation with the Remund-Long sea ice mapping algorithm for SeaWinds and the Special Sensor Microwave/Imager National Aeronautics and Space Administration Team 30% ice edge, and are verified with RADARSAT-1 ScanSAR imagery. The resulting sea ice maps exhibit high edge detail, preserve polynyas and ice bodies disjoint from the primary ice sheet, and thus are suitable for use with wind retrieval and sea ice studies. Principles employed in the algorithm may be of interest in other classification studies.
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Ande et al. (2005) studied this question.
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