Assessment of the agricultural water budget in southern Iran using Sentinel-2 to Landsat-8 datasets

This paper is a first attempt to compute the total water needs of an agricultural plain with remote sensing and ground data in Iran. The cropping areas were mapped with Sentinels-2 images, based on NDVI profiles classification. This model was validated and 85% of the areas were correctly classified. Second, the crop water needs were computed using PYSEBAL and Landsat-8 images. Crop evapotranspiration (ETseason) and Irrigation Requirements (IRseason) were calculated for each crop and then validated by comparing IR collected in the field from farmers with computed IRPYSEBAL on 5 plots. IRPYSEBAL underestimated the reality with an average of 10% while the overestimation average was 17%. The second validation was the comparison of Daily ET from FAO-56 method and Daily ET PYSEBAL showed a RMSE of 0.67 mm/day and MAE of 0.52 mm/day, which assesses the accuracy of PYSEBAL. ETseason varies according to weather parameters in the plain and IRseason, according to different irrigation practices. The most water demanding crops were identified: rice (IR: 1427 mm) and corn (669). The total water balance of Marvdasht was negative in 2018 with 0.2859 km3 of extracted groundwater for irrigation for only 0.098 km3 of available water for aquifers recharge.