Polar sea ice mapping using SeaWinds data

Microwave remote sensing provides an excellent means for mapping polar ice extent. In this study, a new algorithm for polar sea ice mapping is developed for use with the SeaWinds instrument. The approach utilizes a priori information within the framework of Bayes detection to produce sea ice extent maps. Statistical models for sea ice and ocean are represented in histograms which are filtered using a principal component (PC) based filtering technique. Spatial a priori information is incorporated through the loss terms associated with Bayes risk. Sea ice extent maps produced by the algorithm correlate well with the Remund-Long algorithm.