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This article discusses the use of spaceborne polarimetric L-band and C-band synthetic aperture radar (SAR) data for sea-ice detection and classification. The benefits of combining L-band with C-band polarimetric quantities for supervised sea-ice classification in the Eastern Weddell Sea, Antarctica, are investigated. In the experiments, we compared the performance of a maximum likelihood (ML) classifier when used with the combined preferred polarimetric parameters and the individual ones, respectively. The relation between the classification accuracy and the preferred number of polarimetric parameters for classification was examined as well as whether principal component analysis (PCA) and locally linear embedding (LLE) can be used to reduce the dimensionality of the parameter sets. Combining dual-frequency polarimetric quantities improves classification accuracy compared to using individual single-frequency polarimetric quantities. By increasing the dimensionality of the preferred polarimetric parameter sets, the classification using high dimensionality can either result in improvements over the smaller subsets or result in no significant differences. Therefore, using all available polarimetric quantities over the study region is recommended. Further, data fusion with a PCA-based approach is found to be beneficial for sea-ice detection and classification, and poor results have been produced with an LLE-based approach. [ABSTRACT FROM PUBLISHER]