In this paper we used two methodologies of remote sensing data and its derived variables for the estimation of Evapotranspiration (ET). In the first method, the sensible heat flux was calculated by combining air temperature and the remotely sensed surface temperature using Thornthwaite method. We applied and evaluated the ET successfully in the AlAin area of United Arab Emirates. In the Second method, vegetation index derived using Landsat 8 OLI was used for the determination of surface resistance for latent heat. To derive the predicted ET using Normalized Difference Vegetation Index (NDVI), regression analyses were conducted between data derived from satellites, published field meteorological stations data and ET values. From the collected variables of interest, we have also studied the bivariant density estimation curves. It is evident from the patterns of multimodal data that the data belong to different locations with different ET status. It was also observed that wind velocity (U) seems to be decreasing with increasing ET and rest all variable were increasing with increasing ET, which depend on the saturation vapor pressure (SVP). From this approach, we confirmed that the prediction of ET is achievable from the remote sensing data. It is also confirmed that the predicted ET results gained from the NDVI regression functions were comparable to the ET values obtained by the previously published field data. The results showed that indirect application of remotely sensed vegetation index could be used for the ET determination.