The estimation of surface soil moisture status and evapotranspiration from optical remote sensing using the vegetation index–surface temperature () relationship is severely hampered in regions with strong topography, due to the influence of altitude and terrain orientation on surface temperature. In our study, a new empirical approach to normalize surface temperature for terrain elevation—a stratified linear regression model—is presented and is applied on moderate-resolution imaging spectroradiometer (MODIS) data over Calabria, Italy. The method incorporates remotely sensed land surface temperature, a vegetation index, and a digital elevation model. The influence of the newly developed normalization on the relationship and on a soil dryness index is compared to the influence of two existing normalization methods: one using a standard lapse rate of 0.65 K per 100 m and one using a lapse rate derived through simple linear regression between elevation and surface temperature. Stratified linear regression adequately corrects surface temperature while the two other normalization techniques seem to overestimate the actual temperature lapse rate during certain periods of the year. Comparison of a soil dryness index derived using the three different normalization methods with limited in situ soil moisture data results in a slightly stronger correlation for the stratified linear regression model than for the two other normalization methods. –based soil wetness estimation in mountainous terrains remains, however, limited by other spatially varying factors, including terrain orientation and atmospheric conditions.