Pale terricolous lichens are a vital component of Arctic ecosystems, significantly contributing to carbon balance, energy regulation, and serving as a primary food source for reindeer. Their characteristically high albedo also impacts land surface temperature (LST) dynamics across various spatial scales. However, remote sensing of lichens is challenging due to their complex spectral signatures and large spatial variations in coverage and biomass even within local landscape scales. This study evaluates the influence of pale lichens on LST at local and landscape scales by integrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data. An Extreme Gradient Boosting algorithm was employed to map pale lichen biomass, areal extent, and the occurrence of major plant functional types in the sub-arctic heath tundra landscape in the Jávrrešduottar and Sieiddečearru areas on the Finland-Norway border. Generalized Additive Models (GAMs) were used to elucidate the factors affecting LST. The UAV model accurately predicted pale lichen biomass (R