عنوان مقاله [English]
Since a significant amount of precipitation in the agricultural watersheds is used by evapotranspiration, so this parameter plays an important role in water budget and water resources management. Therefore, accurate estimation of this component is very important. SEBAL (Surface Energy Balance Algorithm for Land) belongs to the methods of estimating the actual evapotranspiration (ETact) which is based on remote sensing technique. In the structure of this algorithm several indices and empirical coefficients are used, each of them is effective for estimating actual evapotranspiration. In this study the effect of the uncertainty caused by the soil adjusted vegetation index (SAVI) values in the form of SEBAL algorithm was investigated on ETact prediction on the part of Neyshabour watershed. To determine the optimize value of SAVI, 2013 and 2014 MODIS satellite images were used and the effect of SAVI values uncertainty on ETact prediction was discussed in 2014. Comparison between estimated values and microlysimeter measurements of ETact in the discussed polygon showed that the minimum and maximum error of ETact estimation has been obtained for the amount of 0.2 (R2=0.99, RMSE=0.07) and 1 (R2=0.12, RMSE=0.6) for L coefficient in the SAVI index structure. The results of t-test also showed the significant differences at 95% (p<0.05) between measured and predicted values of ETact, using the whole rangeof L coefficient, except 0.2.