Accurate estimation of actual crop evapotranspiration (ETa) is crucial for effective irrigation management, especially in regions facing growing water scarcity. This study evaluates the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) in a Mediterranean citrus orchard using remote sensing and Eddy Covariance (EC) data. The model was calibrated using local flux tower data from 2021 to 2022. The results show strong agreement between observed and modeled ETa during the wet season, with excellent statistical metrics (R2 = 0.89 and 0.85; r = 0.95 and 0.92; RMSE = 0.95 mm day-1 and 0.91 mm day-1; bias = -0.94 mm day-1 and 0.53 mm day-1 for 2021 and 2022, respectively), confirming the reliability of SAFER under well-watered conditions. However, the model performance decreased significantly during the dry season, R2 = 0.352 and 0.167; r = -0.593 and 0.408; RMSE = 0.86 mm day-1 and 0.68 mm day-1; bias = 0.01 mm day-1 and 0.38 mm day-1 for 2021 and 2022, respectively, likely due to the limited capacity of vegetation indices to detect plant physiological stress under water deficit conditions. SAFER detected spatial variability in ETa across the orchard, highlighting its potential for irrigation zoning. Comparisons with studies in tropical and semi-arid regions demonstrated consistency in mid-season ETa estimates, supporting the model's adaptability. Despite reduced accuracy under drought conditions, SAFER remains a cost-effective and reliable tool for ETa monitoring during optimal growth periods. Overall, it shows strong potential as a remote sensing-based tool for sustainable crop management, though dry-season applications require additional stress-adjustment factors.