Evapotranspiration (ET) is a major source of water depletion in arid and semi-arid environments; and it is a poorly quantified variable in the hydrological cycle. Remote sensing has the potential application to quantify this variable especially at large scale. The present study reports methodology useful to determine whether derived variables from remotely sensed data, such as vegetation and soil brightness indices, could be used to predict ET. To achieve this goal, various regression analyses were conducted between data derived from satellites, field meteorological stations, and ET values. Selected sub-scenes of Landsat Enhanced Thematic Mapper images free of cloud were used to derive Normalized Difference Vegetation Index (NDVI) and Soil Brightness Index using ER-Mapper and JMP software packages. From the obtained relationship between NDVI and ET, it was observed that ET increases sharply with increase in NDVI. The predicted ET results obtained from the multiple regression functions of field ET, NDVI, solar radiation, wind velocity, and/or temperature are comparable with the ET values obtained by Penman-Monteith method. The results showed that a remotely sensed vegetation index could be used, indirectly, to determine ET values. However, there is still considerable work to be done before simple and full automated extraction of ET from the reported methods can be achieved for large-scale applications.