The performance of thermal-based models for estimating sensible (H) and latent (LE) heat fluxes over semiarid forests is still not well documented, largely due to the difficulty of accounting for canopy height (hc) effects on satellite land surface temperature (LST). This study addresses this limitation by integrating LiDAR-derived hcinto the classical contextual method, which combines Landsat-derived LSTand green vegetation fraction. Landsat LSTis first normalized to remove the influence of hc, and the resulting normalized LSTis then used to estimate Hand LEusing the classical contextual approach. The method is applied over a nine-year period in two Mediterranean forest sites with eddy covariance stations-Puechabon and Fontblanche. The threshold canopy height (hcmax), above which LSTbecomes insensitive to turbulent fluxes, is estimated as hcmax = 42 +/- 4 m for Puechabon and hcmax = 35 +/- 3 m for Fontblanche. For both sites, the normalization of LST for hceffect significantly improves the correlation between remotely sensed and in situ H(LE) measurements from 0.40 (0.06) to 0.72 (0.43), respectively. Moreover, by setting the dry edge by a simple soil energy balance model, the bias between remotely sensed and in situ H(LE) measurements is much reduced from-163 (+132) W.m-2 to-56 (+25) W.m-2, and the slope of the linear regression much closer to 1 from 0.28 (0.07) to 0.84 (0.60), respectively. This is the first study to incorporate LiDAR-derived hcinto contextual methods, significantly improving thermal-based estimates of turbulent fluxes in forested semi-arid environments.