Mapping wetland types in northern-latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel-1, Sentinel-2 and terrain derivatives derived from Copernicus DEM to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regionalized classification models constructed from region-specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel-1, Sentinel-2 and terrain derivatives). Overall accuracy for the individual sites ranged between 84% to 92%. Class specific accuracies for the peatland types were also high overall, but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that the regionalized classification models consistently outperformed the global classification by producing significantly higher classification accuracies for all five sites. This opens for promising progress in terms of identifying effective approaches for stratifying northern-latitude areas for continental scale peatland classification.