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We evaluate the potential of combined Advanced Land Observing Satellite Advanced Visible and Near-Infrared (AVNIR-2) and fully polarimetric Phased-Array-type L-band Synthetic Aperture Radar (PALSAR) data for land cover classification. Optical AVNIR-2 and fully polarimetric PALSAR can provide both surface spectral information and scattering information of the ground surface. The fully polarimetric PALSAR is particularly important for land cover classification because quad-polarization PALSAR data and its polarimetric parameters contain additional surface information. As a consequence, by combining optical AVNIR-2, PALSAR, and polarimetric parameters into a single data set, land cover classification accuracy may be further improved. For efficient and convenient handling of the combined multisource data, we used a subspace method for the classification and estimated its classification capability for various combinations of optical, PALSAR, and polarimetric parameter data sets in the Lake Kasumigaura region of Japan. We also compared the results obtained using the subspace method with those obtained by the support vector machine (SVM) and maximum-likelihood classification (MLC) methods. The classification results confirm that, when the combined optical AVNIR-2, PALSAR, and polarimetric coherency matrix data were used, the classification accuracy of the subspace method was better than that when other data combinations were used. The subspace method also performed better than the SVM or MLC method in high-dimensional data set classification. Moreover, the experimental results demonstrated that the proposed subspace method is robust for data classification when there is data redundancy and thus allows optimal feature selection procedures to be avoided.