Plant functional type (PFT) fractional cover (fCover) information is used to assess vegetation composition and diversity, which is crucial for understanding the health and functioning of Arctic ecosystems. Satellite data provide a means for scaling local, plot-measured fCover to regional scales. We harmonized open-access vegetation plot data repositories that provide fCover observations in Alaska: the Alaska Vegetation Plots Database, ABR Inc.—Environmental Research and Services, the Arctic Vegetation Archive, and the National Ecological Observatory Network, and the plot samples collected at the Seward Peninsula. Leveraging these plot datasets, we generated a wall-to-wall map of fCover for tundra PFTs at the north slope of Alaska. While the plot fCover provides invaluable information about the tundra vegetation, they contain large inconsistencies in terms of sampling scale (species- or PFT-level), plot size (0.5 to 55-m in radius), collection year (2010–2021), field sampling approaches (point- or quadrat-intercept), and fCover measurement (percent or Braun-Blanquet codes). We harmonized the plot fCover using a consistent PFT schema: litter, lichen, bryophyte, forb, graminoid, deciduous and evergreen shrub, suitable for parameterizing terrestrial land surface models. We then linked the plot fCover with satellite-derived explanatory variables (Sentinel-2 spectra, Sentinel-1 polarization, and topography-related features) and trained random-forest (RF) regression models to map wall-to-wall fCover for each PFT. To reduce the spatio-temporal inconsistencies between plot and satellite observations, we adopted a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our results suggest that the RF regression models can estimate fCover with good accuracy. The correlations between plot-observed and satellite-derived fCover reach high R² and low bias when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20m resolution, providing the information needed for improved representation of Arctic tundra vegetation in the Earth system modeling to better understand climate-vegetation feedback in the Arctic tundra ecosystem.