Applicability of GLDAS precipitation dataset in estimating green and blue water footprints of wheat and maize in Qazvin plain station using Aqua Crop model

Remote sensing technology, due to its valuable features such as taking multi-temporal and multi-spectral images, appropriate and varied temporal resolution and radiometric capability, wide and integrated view of the region, can be used in many agricultural tasks such as crop yield prediction, soil moisture measurement, information about the amount of drought and frost should be effective. In this study, to evaluate the networked meteorological datasets GLDAS-AgMERRA, GLDAS-CRU, GLDAS-AgCFSR in estimating the blue and green water footprints of wheat and blue water footprints of maize and comparing it with the estimated values of Qazvin plain during 1980-2010. Statistical evaluation was performed using R2, NRMSE and ME indices. The average total water footprint of wheat in study area was estimated at 869 (m3/ton) in which the share of green water footprint was 47% and the share of blue water footprint was 53%. Estimated data in scenarios 1 (Precipitation and evapotranspiration of GLDAS-Temperature of CRU) and 3 (Precipitation of GLDAS-Temperature and evapotranspiration of CRU) for green water footprint of wheat and for blue water footprint, scenario 5 (Precipitation and evapotranspiration of GLDAS-Temperature of AgCFSR) and Scenario 3 have the highest correlations for wheat and maize, respectively. The results show that these datasets are not suitable for estimating the water footprint of maize plants, but for estimating the green and blue water footprints of wheat plants, the information of networked meteorological datasets can be used with appropriate accuracy.