Abstract. Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (S_r^spectra≈0.3, S_r^gonio≈0.3, and S_r^AATSR≈0.5), and no improvement using mono-angular sensors (S_r≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSE_grass=0.50 K, RMSE_wheat=0.29 K, RMSE_{sugar beet}=0.75 K, RMSE_barley=0.67 K); but for low LAI values the results were unsatisfactory (RMSE_{young maize}=2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepancy.