The Advanced Science and Technology Institute of the Department of Science and Technology (DOST-ASTI), through its Synthetic Aperture Radar and Automatic Identification System (SARwAIS) Project, has gained access to S-Band SAR images acquired by the NovaSAR-1 satellite of UK’s Surrey Satellite Technology Ltd. (SSTL) To help maximize the utility of these images especially in the aspect of terrain-related applications, their viability as potential alternatives to datasets like optical satellite images and other SAR images in characterizing land cover types was evaluated. Statistical analyses on the backscatter values from the tri-polarization ScanSAR datasets using Multivariate Analysis of Variance (MANOVA) and its corresponding post-hoc tests showed that there is a significant difference on the mean backscatter values at 0.05 level of significance. Moreover, Tukey’s honestly significant difference (Tukey’s HSD) test determined which pairs contribute to the significant difference. Using the Random Forest algorithm resulted in an accuracy of 66.93% without further optimization and/or reduction in the number of land covers being classified. Despite the relatively unremarkable accuracy score, it still showed potential for data augmentation with optical satellites for land cover mapping and other terrain-related applications.