This study evaluated supervised land cover classification using PALSAR polarimetric interferometry (PolInSAR) data. Seven land cover classes were classified - water, paddy, crop, grass, forest, urban, and bare land. Classification accuracy was compared for four datasets: Quad-PolInSAR, Dual-PolInSAR, Quad-PolSAR, and Dual-PolSAR. Accuracy was highest for Quad-PolInSAR and lowest for Dual-PolSAR. Classification using a support vector machine (SVM) was more accurate than a Wishart classifier. PolInSAR classification performed best for forests, urban areas, bare land and water, while an optical land cover