Measurements of land surface temperature (LST) performed in the thermal infrared (TIR) domain are prone to strong directional anisotropy. Instead of detailed analytical physical TIR models requiring too much input information and computational capacities, simplified parametric approaches capable to mimic and correct with precision the angular effects on LST will be deemed suitable for practical satellite applications. In this study, we present a simple two parameters model, so-called RL (Roujean-Lagouarde), which shows capabilities to properly depict the directional signatures of both urban and vegetation targets within an accuracy better than 1 °C. This latter value is the RMSE (root mean square error) obtained as the best adjustment of the RL model against in situ datasets. Then the RL approach was compared to a synthetic dataset generated by the model Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) in which large variability in meteorological scenarios, canopy structure and water status conditions was accounted for. Results indicate RMSE ≤ 0.6 °C which is a very hopeful result. Besides, the RL model performs even better than the popular parametric model of Vinnikov that encompasses two unknowns. The ability of RL model to better reproduce the hotspot phenomenon explains this feature. The RL model appears as a potential candidate for future operational processing chains of TIR satellite data because it fulfills the requirements of both simple analytical formulation and limited number of input parameters. Efforts nevertheless remain to be done on inversion methodologies.Read less <