Remote sensing (RS) technology can be effective in many agricultural activities due to the associated valuable features such as the ability to take multi-time and multi-spectral images, the ability to distinguish between time and radiometry, and a wide and integrated view of the area. The RS technology also could help to estimate the actual evapotranspiration and to investigate the crop water productivity. In this study, the effect of precipitation, temperature and potential evapotranspiration of the GLDAS model on the outputs of Aqua Crop model in Qazvin synoptic station for two wheat and maize products from 1979 to 2013 have been investigated. Also, the parameters of GLDAS model, the precipitation during 1979-2015 and the evapotranspiration during 1979-2013 were examined. The Penman Monteith method was used to compute the potential evapotranspiration of Qazvin station. The results of the GLDAS model, the precipitation model data and station data, R2 = 0. 97 and NRMSE = 0. 38 show that there is a high determination coefficient between these two data sets. The statistical results show R2 = 0. 99 and NRMSE = 0. 10 between the evapotranspiration data obtained from the GLDAS model and station data. The results of the statistical evaluation of the outputs of Aqua Crop model, Qazvin station data and the GLDAS model for maize and wheat products showed that the model is more accurate in biomass and yield according to the RMSE and NRMSE indices.