With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at <0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.