The availability of multi-frequency and multi-seasonal SAR data within the data catalog of web-based cloud-computing geospatial analysis platforms opens new prospects and dimensions for executing large-scale, cloud-free, and continuous earth observation activities, such as those for SAR-based land cover classification purposes. The performance of C-band Sentinel-1 SAR data, L-band ALOS-2 PALSAR-2 ScanSAR data, and their combination, taken on dry, wet, and multi-seasonal, was compared and investigated to classify land cover classes in Central Kalimantan, Indonesia. Overall, the combined C- and L-band multi-seasonal SAR data produced the highest accuracy indicators for OA (78.30%) and Kappa (0.73). Conversely, the L-band dry-season SAR data exhibited the lowest accuracy, i.e., 57.02% of OA and 0.46 of K. Furthermore, accuracy indicators for land cover classifications utilized multi-seasonal datasets were superior to those only implemented with dry- or wet-season datasets. In addition, integrating C- and L-band SAR data as inputs for land cover classification enhanced its accuracy compared to using them individually. The increments were around 6.12–17.10% of OA and 8.70–24.24% of K for the C-band and approximately 22.30–35.35% of OA and 32.67–53.77% of K for the L-band. To conclude, the multi-frequency and multi-seasonal SAR data proved to be the optimal option among all combinations of datasets for generating SAR-based land cover classification in tropical ecosystems, which was evidenced by the OA and K, particularly when advantaging a web-based cloud-computing geospatial analysis platform such as the Google Earth Engine demonstrated in this work.