Hyper-arid regions, such as the Sultanate of Oman, require precise information regarding actual water application to develop efficient irrigation management. Remote sensing techniques with imagery models can be used as practical tools in irrigation practices by estimating the actual evapotranspiration (ETa) at local and regional scales. In this study, spatial and temporal changes in the ETa were evaluated by investigating the performance of three different models, including two surface energy balance models: surface energy balance algorithm for land (SEBAL) and mapping evapotranspiration at high-resolution internalized calibration (METRIC), as well as an agro-hydrological model: soil, water, atmosphere, and plant (SWAP), for the hyper-arid conditions of Oman. This study is unique in its field application, as very few studies have conducted ETa estimations using satellite imagery in the hot and hyper-arid regions of Oman. In situ meteorological data were integrated with Landsat-8 operational land multispectral imagery and thermal infrared sensor satellite images. Further, a sensitivity analysis comparing the three models' estimated ETa with in situ sap flow field measurements (SFM) and the Penman-Monteith (PM) model. The results showed that the SEBAL model overestimated the sensible heat flux compared with the METRIC model. Although the PM model showed a higher correlation (R2 = 0.87) with the SFM, the METRIC model, among all the investigated models, showed the highest correlation of R2 = 0.83. Further, the SEBAL and METRIC models overestimated the ETa, especially in the summer. Conversely, SWAP underestimated the ETa, as compared with the SFM. These results show that the METRIC model estimates the ETa with high spatial and temporal accuracy. Meanwhile, the SWAP model was the least suitable model for estimating the ETa under the hyper-arid condition of Oman. These findings will act as a reference for accurate water requirements, thereby linking water resource management and future irrigation scheduling in local hot and hyper-arid conditions.
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