The disproportionate warming in the Arctic and the resulting adverse ecosystem changes underline the importance of continued monitoring of these ecosystems. Land-cover classification maps of the Arctic regions are essential for monitoring and change detection purposes, as well as upscaling of various ecosystem processes. However, large-scale land cover maps of the Arctic regions are often too coarse to properly capture the heterogeneity of these landscapes. In this study, we bridge this gap through incorporating multi temporal Landsat-8 OLI data in a large-scale land cover classification, and subsequently produce a tundra classification map for the entire Greenland. An algorithm is developed that allows for the extraction of vegetation phenology from single-year time series of 4169 OLI scenes at 30 m resolution despite the low revisit frequency of the satellite and persistent cloud cover. The phenological metrics, satellite-derived wetness, and terrain information are then used to separate land surface classes using a random forest classifier. The optimal algorithm parameters and input layers are identified, ultimately yielding a cross-validation accuracy of 89.25% across the studied area. Finally, we have conducted a comprehensive analysis on the resulting land-cover map and for the first time presented the geographical distribution, latitudinal gradients, and climate linkages of the various tundra vegetation classes across the ice-free part of Greenland. With a resolution of 30 m and Greenland-wide spatial coverage, the produced land-cover map can support various applications at scales ranging from the landscape to regional level.