Wildland fire is common and widespread in Alaskan tundra. Tundra fires exert considerable influence on local ecosystem functioning and contribute to climate change through biogeochemical (e.g. carbon cycle) and biogeophysical (e.g. albedo) effects. These treeless landscapes are characterized by a high degree of variation in fuel loading at scales much finer than moderate (30 m) satellite observations. However, because of the remoteness of the tundra and its lower contribution to carbon release compared to boreal forests, most frequently tundra fuels are poorly characterized, limiting the effective development of tundra-specific fire occurrence and behavior models. This study presents an approach to mapping the fractional coverages of major fuel type components in Alaskan tundra circa 2015 combining field data and Landsat 8 Operational Land Imager observations. We adopt a multi-step Random Forest method to estimate the fractional vegetation cover of woody, herbaceous, and nonvascular components at subpixel level. We demonstrate the strong capability of exploiting multi-seasonal spectral information to identify these component types, with R-squared values around 0.95 and root mean squared errors below 10% for predicting their fractional cover. Our mapping products depict the spatial distribution of woody, herbaceous, and nonvascular components at subpixel resolution across Alaskan tundra, which can function as a critical input for studying wildland fire risk and behavior in the tundra. The distributions of these fuel components align well with climate-based tundra ecoregions although climate variables are not included in our models.