Reindeer lichens (Cladonia spp.) are an essential food source for caribou especially during winter. They can also be a valuable indicator for ecosystem health and climate change. Inventory of lichen abundance at regional scales is required to assess availability within caribou ranges, and assess potential declines from natural and anthropogenic disturbances. Previous studies have mapped lichen cover and volume using remote sensing, but these efforts were often constrained by the limited availability of ground truth information needed for model calibration and validation. In this study, we leveraged unoccupied aerial vehicle (UAV) surveys and WorldView (WV) satellite scenes in a nested upscaling approach in order to expand the number of training samples at the 30 m Landsat resolution. These were used to develop machine learning models to map fractional reindeer lichen cover in Eastern Canada. We found that the best correlation between UAV and WV derived lichen coverages exists at an optimal scale that is slightly larger than 30 m and varies with landscape type and observation geometry. Based on training data from UAV-calibrated lichen coverage from WV data, a neural network model with simple structure achieved a root mean square error (RMSE) = 0.09, a mean absolute error (MAE) = 0.07 and R2 = 0.79 for mapping fractional lichen cover from Landsat without the use of ancillary data. We then applied our model and Landsat data to produce a lichen fractional cover map for the Red Wine Mountain caribou herd range in Labrador, NL and the Manicouagan caribou herd range in Québec. Validation against domain-averaged lichen cover in eight UAV survey sites suggests an accuracy with RMSE = 0.04, MAE = 0.03 and R2 = 0.62 for low lichen cover. Compared to aggregated lichen cover at 30 m from UAV surveys, map accuracy decreases to RMSE = 0.09, MAE = 0.06, and R2 = 0.49, partially due to registration error between UAV and Landsat images. Our study demonstrates that upscaling of lichen cover from UAV data to Landsat via an intermediate image scale is an effective regional-scale mapping approach.