The pronounced impact of topography on meteorological conditions has largely limited evapotranspiration (ET) remote sensing techniques to relatively flat terrains. This study addresses this limitation by adapting and assessing the performance of two common ET models based on thermal infrared data in rugged mountainous regions: a physically-based energy balance model (TSEB-PT), and a contextual model (LST-VI). The latter derives the evaporative fraction (EF), defined as ratio of the latent flux (LE) to available energy, from spatial relationships between land surface temperature (LST) and Vegetation Index (VI), by assuming uniform meteorological conditions. The LST-VI model hence requires the normalization of LST data for meteorological variability effects induced by topography prior to EF estimation, while TSEB-PT requires the spatialization of meteorological data at the thermal sensor's resolution. This study provides for the first time a quantitative assessment of methods for correcting topographical effects at thermal data resolution within a steep-sided valley, and compares them when applied to EB-and EF-based models. Both ET models are applied to 30 m resolution Landsat data across a 20 km by 44 km area in the High Atlas mountain of Morocco from 2020 to 2022. The models' results are evaluated at two eddy covariance sites with or without considering topographic effects: an agricultural foothill site, and an elevated rocky site, located at 900 and 3850 m.a.s.l., respectively. By taking into account topography, the RMSE (and % error) on simulated LE at the foothill site was reduced by 29 W/m 2 (29 %) and 10 W/m 2 (16 %) for TSEB_PT and LST-VI respectively. At the elevated site however, the RMSE (and % error) reduction was 50 W/m 2 (50 %) and 64 W/m 2 (59 %) for TSEB_PT and LST-VI respectively. Analysis of the spatial variability over the study area indicates that the EF distributions (corrected for topographical effects) between east-facing and westfacing slopes are similar for LST-VI (mean difference of 0.01) and significantly different for TSEB_PT (mean difference of 0.19). Normalizing LST for topographic effects at the thermal sensor resolution is hence an effective way of estimating ET in mountains despite the inherent uncertainties in the available meteorological data.