Preliminary Evaluation of Remote Sensing Evapotranspiration Models for Field-Scale Agricultural Water Management in Arid Central Iran

Authors

• Somayeh Sima, Utah State UniversityFollow
• Iman Raissi Dehkordi, Tarbiat Modares UniversityFollow
• Mohammadhosein Taghikhani, Tarbiat Modares UniversityFollow
• Neamat Karimi, Water Research InstituteFollow

Document Type

Article

Author ORCID Identifier

Somayeh Sima https://orcid.org/0000-0001-5232-6756

Neamat Karimi https://orcid.org/0000-0002-7896-4390

Journal/Book Title/Conference

Agricultural Water Management

Volume

323

Publisher

Elsevier BV

Publication Date

1-6-2026

Journal Article Version

Version of Record

First Page

1

Last Page

16

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Abstract

Accurate estimation of actual evapotranspiration (ET_a) is crucial for effective water resource management and optimizing agricultural yields. While satellite-based surface energy balance ET_a models are widely adopted, their field-scale accuracy in under-researched regions, such as Iran, remains a critical knowledge gap. This study assesses five prominent models—PySEBAL, PyMETRIC, SSEBop, PyTSEB, and ETLook (from FAO’s WaPOR v.2, L1 product)—for daily ET_a estimation over an alfalfa field in the arid central part of Iran. Models were adjusted for the field using in situ weather data and Landsat-8 images, and validated against the scintillometer data. Results showed SSEBop provided the most accurate ET_a estimates (KGE = 0.83), closely followed by PyMETRIC, TSEB, and PySEBAL (KGEs ≥ 0.73). Conversely, ETLook performed poorly and failed to capture spatial ET_a variations. A significant performance enhancement was achieved (RMSE= 0.34 mm day^⁻¹ and KGE= 0.90) by an ensemble mean of models. We further demonstrate that two-source ET_a models do not inherently outperform one-source models, likely due to greater parameter uncertainty. We emphasize the importance of considering irrigation, harvest, and oasis effects for accurate model application. All evaluated models, except ETLook, were found to meet the recommended accuracies for on-farm irrigation management. This study sheds light on the selection of sophisticated field-scale ET_a models for agricultural water management, while considering the dynamism of irrigation and harvest. Our findings provide critical insights for the operational application of remote sensing ET_a models and promoting smart agriculture in arid agricultural settings.

Recommended Citation

Sima Somayeh, Dehkordi Raissi Iman, Taghikhani Mohammadhosein, Karimi Neamat, "Preliminary Evaluation of Remote Sensing Evapotranspiration Models for Field-Scale Agricultural Water Management in Arid Central Iran," Agricultural Water Management, 323 (2026), https://doi.org/10.1016/j.agwat.2025.110084