Comparative Analysis of Three Remote Sensing Models for Estimating Actual Evapotranspiration in the areas flood water spreading of Garbaygan Fasa

Document Type : Research

Authors

1 Assistant Professor, Forsts Rangelends and Watershed Management Department, Bushehr Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Bushehr, Iran

2 Assistant Professor, Soil Conservation and Watershed Management Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

3 Associate Professor, Soil Conservation and Watershed Management Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

4 Associate Professor, Agricultural Engineering Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

10.22092/wmrj.2025.371213.1642

Abstract

Introduction and Goal
Sustainable water management in arid and semi-arid regions requires accurate estimation of water balance components, especially actual evapotranspiration (ETa). In irrigated agriculture, actual evapotranspiration is particularly crucial for understanding water consumption by vegetation. In recent years, due to the limitations of field-based ETa measurements, satellite remote sensing models such as METRIC, SEBAL, and SEBS have gained increasing importance. This study aims to evaluate and compare the performance of these three models in estimating ETa over the Garbayegan Plain, Fars Province, Iran.
Materials and Methods
The study was conducted in the Garbayegan Plain, located in southeastern Fars Province with an arid climate and an average annual rainfall of about 230 mm. Landsat 8 and 9 satellite imagery from the 2022–2023 agricultural season were used. After applying geometric, radiometric, and atmospheric corrections, the SEBAL, METRIC, and SEBS models were implemented based on the surface energy balance equation. Using these models, net radiation, soil heat flux, sensible heat flux, and latent heat flux were estimated, and instantaneous and daily ETa were subsequently to calculate. Field data, soil water balance, and irrigation records were used for validation. NDVI indices were applied to extract agricultural pixels and to evaluate crop water consumption. 
Results and Discussion
The results showed that the highest accuracy in ETa estimation was achieved by the METRIC model (R² = 0.87, RMSE = 0.85 mm/day), primarily due to its use of internal calibration via anchor pixels. The performance of the SEBS model was also acceptable (R² = 0.79). Although SEBAL is structurally similar to METRIC, its lack of local calibration resulted in lower accuracy (R² = 0.74). Nevertheless, SEBAL produced more detailed spatial maps of ETa. The selection of an appropriate model for ETa estimation depends on the intended application, the availability of field data, and the required numerical or spatial accuracy. The METRIC model, when combined with reference pixel calibration, was found to be more suitable for regional assessments at the farm scale. On the other hand, SEBAL and SEBS can provide useful preliminary spatial insights under limited field data conditions. The superior performance of METRIC highlights the importance of integrating ground observations with satellite imagery.
Conclusion and Suggestions
The findings of this study indicate that the METRIC model is a reliable tool for accurately estimating ETa in agricultural lands of the Garbayegan Plain. It can be used to monitor water consumption, support irrigation scheduling and evaluate water use efficiency. Furthermore, NDVI-based pixel classification significantly improved estimation of agricultural water consumption. It is recommended that future studies employ satellite imagery with high-temporal-resolution satellites (e.g., Sentinel-2) for model calibration and validation, along with a greater integration of field observations.

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References

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History

  • Receive Date: 31 October 2025
  • Revise Date: 23 November 2025
  • Accept Date: 21 December 2025

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