Unmanned aerial vehicles (UAVs)-based Light Detection and Ranging (Lidar) scanners, and optical imagery may facilitate a precisely description of atmospheric-surface processes in both high spatial and temporal resolution without being confined to a specific footprint. By monitoring the 3-d structure of a land, the reflected and thermal radiation using the current available technology we aim to evaluate a conceptual approach to infer turbulent heat fluxes over different agricultural fields in Denmark where an Integrated Carbon Observation System (ICOS) Class 1 station data will be utilized to validate the drone-based heat fluxes. Based on preliminary results the objectives of this study are to: i) reliably partition the point cloud data produced by the UAV-Lidar system into bare-earth and vegetation structure; ii) assign the aerodynamic resistance based on the retrieved geometry of the canopy and surface roughness models; iii) calculate the sensible heat flux and available energy using thermal and spectral reflectance maps; iv) estimate the spatial distribution of latent heat flux by applying a surface energy balance model. Mapping the energy fluxes using Lidar scanners, aerial photogrammetry and drones may narrow considerably the spatial and temporal gap in data between ground and space borne/aircraft measurements, thus providing more reliable surface energy budget on large spatial scale, and enabling the application of environmentally sustainable irrigation, fertilization and natural ecosystem restoration practices.