The spatial extent of northern peatlands remains highly uncertain in spite of rapidly developing satellite observation datasets. This is limiting progress in the understanding of fundamental biogeochemical processes, such as the global carbon (C) cycle and climate feedback effects on C fluxes. In this study, we evaluated the capabilities of two new satellite datasets that enable regional scale mapping of peatland extent at high spatial resolution, including Sentinel-1 synthetic aperture radar (SAR) and the Arctic digital elevation model (ArcticDEM). Terrain indices and temporal features derived from these datasets provided input to Random Forest models for delineating four main land cover classes (forest, open upland, water and peatland) in an area in northern Sweden consisting of both lowland and mountainous terrain. The contribution of ArcticDEM to the classification accuracy was assessed by comparing the results with those derived when a high quality LiDAR based DEM (LiDEM) was used as alternative model input. This study shows that multi-seasonal SAR alone can produce reasonable classification results in terms of overall accuracy (OA; 81.6%), but also that it has limitations. The inclusion of terrain indices improved classification performance substantially. OA increased to 87.5% and 90.9% when terrain indices derived from ArcticDEM and LiDEM were included, respectively. The largest increase in accuracy was achieved for the peatland class, which suggests that terrain indices do have the ability to capture the features in the geographic context that aid the discrimination of peatland from other land cover classes. The relatively small difference in classification accuracy between LiDEM and ArcticDEM is encouraging since the latter provides circumpolar coverage. Thus, the combination of Sentinel-1 time series and terrain indices derived from ArcticDEM presents opportunities for substantially improving regional estimates of peatland extent at high latitudes.