Leaf Area Index (LAI) is considered to be a key parameter of ecosystem processes and it is widely used as input to biogeochemical process models that predict net primary production (NPP) or can be a useful parameter for crop yield prediction and crop stress assessment as well as estimation of the exchanges of carbon dioxide, water, and nutrients in forests. LAI can be derived from satellite optical data using models referred to physical-based approaches, which describe the physical processes of energy flow in the soil-vegetation-atmosphere system, and models using empirically derived regression relationships based on spectral vegetation indices (VIs). The first category of models are more general in application because they can account for the different sources of variability, although in many cases the information needed to constrain model inputs is not available. In contrast, empirical models depend on the site and time. The aim of this paper is to create a reliable semi-empirical method, applied in two Mediterranean sites, to estimate LAI with high spatial resolution images. The model uses a minimum dataset of a Landsat 5 TM or SPOT 4 XS image, land cover map and DEM for each area. Specifically, this model calculates the reflectance of initial bands implementing topographic correction with the aid of DEM and metadata of the images and afterwards uses a list of NDVI values that correspond to certain LAI values on different land cover types which has been proposed by the MODIS Land Team. This model has been applied in two areas; in the river basin of Nestos (Greece and Bulgaria) and in the river basin of Tamega (Portugal). The predicted LAI map was validated with ground truth data from hemispherical images showing high correlation, with r reaching 0.79 and RMSE less than 1 m2/m2.