Remote Sensing and GIS Techniques for Irrigation Water Management in North Delta of Egypt under Water Scarcity Conditions

Remote sensing and GIS are effective for assessing crop water use and estimating evapotranspiration rates. This study aims to improve water management planning on selected farms by determining the best estimates of actual evapotranspiration (ETa). The selected site represents small-scale private farms located in the northern Nile Delta, Egypt. Using European Sentinel satellite images of the 2022-2023 periods, ETa values were estimated by three Remote Sensing (RS)-based models including the Surface Energy Balance Algorithm for Land (SEBAL), the Normalized Difference Vegetation Index-based method (ETa_NDVI), and the Simplified Surface Energy Balance (SSEB). The efficiency of these models was compared using FAO-Penman-Monteith (FAO-P-M) as a reference model. The Penman-Monteith approach estimated the ETc to be 2.51, 3.81, 4.15, 3.84, 2.08, 2.06, 2.84, 7.52, 5.98, 5.07, 5.08 and 5.13 mm/day for wheat, clover, potatoes, sugar beet, flax, beans, onion, rice, maize, sesame, sunflower, and cotton respectively, whereas the estimated ETa from SEBAL for these crops were 2.24, 3.50, 4.06, 3.71, 2.08, 2.19, 3.10, 7.34, 5.74, 5.97, 5.14, and 5.37 mm/day respectively. The estimated ETa seasonal averages using ETa_NDVI, SEBAL, and SSEB methods for wheat crop are 2.41, 2.43, 2.24 mm/day respectively. SEBAL achieved the highest R2 value (0.96) and the lowest Root Mean Square Error (RMSE) (0.24 mm/day). In comparison, the ETa_NDVI and SSEB models recorded R² values of 0.74 and 0.85 with RMSE values of 0.79 and 0.65 mm/day, respectively. These results indicate that the SEBAL model is capable of reliably estimating ETa in selected farms, which can improve irrigation planning.