In this study, using surface energy balance algorithm for land (SEBAL), evapotrairation has been estimated for the lands of Zayandehrud river basin (lands under the irrigation networks of left and right Nekouabad, Abshar right and Borkhar plain), Golpayegan plain and Damane-Fereidan in province of Isfahan. SEBAL is a remote sensing algorithm which the instantaneous surface energy balance will perform for each pixel of a satellite image. This algorithm will be estimated using surface temperature, surface reflective, indicators of vegetation status and their internal relations, surface fluxes for various covers of land surface. The satellite images used in this study are the ETM + image with the spatial resolutions of 30 and 60 m for the reflective and thermal bands, respectively and the MODIS image with the spatial resolution of 250, 500 and 100 m. After the required corrections (geometric and topographical), the processing of the region is performed separately and the distribution related to surface albedo, vegetation indicators, surface emissivity, surface temperature, incoming and outgoing radiations, soil heat flux and sensible heat flux for each region in the images is obtained. Finally, the daily evapotrairation is calculated. The actual evapotrairation estimated using SEBAL in the ETM + image with actual evapotrairation obtained from the method of the FAO Penman-Monteith (considering the crop coefficient) compared such that the evapotrairation estimated at two methods for the rice fields of Lenjanat have only 0.22 mm/day differences. The results show that the estimated values (in regional scale) in the ETM + and MODIS images are very close together so that the mean evapotrairation is estimated for each region in the two images are close too. of course, for smaller scales like those of fields, using the MODIS image decreases results’s accuracy because of its non homogeneous pixels (low spatial resolution ability). Using Lane method, the sensitivity analysis is done for the existing parameters in SEBAL and it is found that the parameters such as the normalized difference vegetation index, leaf area index and wind speed have the minimum sensitivity and the incoming short wave radiation and near surface air temperature difference have the highest sensitivity in the model. This sensitivity analysis is only performed on the ETM + image.