Sima et al. (2026) Preliminary evaluation of remote sensing evapotranspiration models for field-scale agricultural water management in arid central Iran

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-01-06
• Authors: Somayeh Sima, Iman Raissi Dehkordi, Mohammadhosein Taghikhani, Neamat Karimi
• DOI: 10.1016/j.agwat.2025.110084

Research Groups

• Department of Civil and Environmental Engineering, Utah State University, Logan, USA
• Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
• Department of Water Resources Research, Water Research Institute, Ministry of Energy, Tehran, Iran

Short Summary

This study evaluates five satellite-based surface energy balance models (PySEBAL, PyMETRIC, SSEBop, PyTSEB, and ETLook) and their ensembles for daily actual evapotranspiration (ETa) estimation over an alfalfa field in arid central Iran, finding SSEBop and an ensemble mean to be most accurate and suitable for field-scale agricultural water management.

Objective

• To evaluate the performance of multiple one-source and two-source remote sensing actual evapotranspiration (ETa) models (PySEBAL, PyMETRIC, SSEBop, PyTSEB, ETLook) and their ensembles for daily ETa estimation over an alfalfa field in the arid central part of Iran.

Study Configuration

• Spatial Scale: Field-scale (12-hectare alfalfa farmland), with primary model outputs at 30 meters spatial resolution (Landsat-8) and one product (WaPOR/ETLook) at 250 meters.
• Temporal Scale: Daily actual evapotranspiration (ETa) estimates from May to September 2016.

Methodology and Data

• Models used: PySEBAL, PyMETRIC, SSEBop, PyTSEB, ETLook (from FAO’s WaPOR v.2, Level 1 product), and ensemble mean, median, and supervised median of the individual models.
• Data sources:
  • Satellite: Landsat-8 images (cloud-free, Collection 2), Harmonized Landsat Sentinel (HLS) data (30 m) for harvest detection, MODIS and PROBA-V data (for WaPOR/ETLook).
  • Observation (in-situ): Scintillometer (LAS BLS900) data for actual evapotranspiration (ETa) and sensible heat flux validation, automatic weather station data (air temperature, wind speed, vapor pressure, relative humidity, radiative energy fluxes), and soil characteristics from Iran’s Agricultural Engineering Research Institute (AERI).

Main Results

• SSEBop provided the most accurate daily ETa estimates (KGE = 0.83), followed by PyMETRIC, PyTSEB, and PySEBAL (KGEs ≥ 0.73).
• ETLook (WaPOR Level 1, v2) performed poorly (KGE = 0.22) and failed to capture spatial ETa variations due to its coarse resolution.
• An ensemble mean of PySEBAL, PyMETRIC, SSEBop, and PyTSEB significantly enhanced performance (RMSE = 0.34 mm day⁻¹ and KGE = 0.90), outperforming individual models.
• One-source models (SSEBop, PyMETRIC, PySEBAL) generally outperformed two-source models (PyTSEB, ETLook) at the same spatial resolution, likely due to greater parameter uncertainty in the latter.
• All evaluated models, except ETLook, met the recommended accuracy thresholds (RMSE < ± 1 mm day⁻¹) for on-farm irrigation management.
• PyMETRIC, SSEBop, and the ensemble mean also met the accuracy requirements (NRMSE < 15%) for seasonal crop water use per parcel and groundwater use management.
• Energy balance closure for PySEBAL, PyMETRIC, and PyTSEB showed marginal residuals (less than 2 W m⁻²).
• Spatial ETa patterns showed higher values towards the field's center, influenced by sparse vegetation at borders and the "oasis effect" from surrounding dry land.

Contributions

• This study is one of the few intercomparison studies of remote sensing ETa models conducted at local and daily scales in Iran.
• It utilized scintillometer measurements for rigorous validation, improving the quality of reference in-situ data compared to previous studies in Iran.
• It provides critical insights for the selection and operational application of field-scale remote sensing ETa models for agricultural water management in arid settings, considering the dynamics of irrigation and harvest.
• It demonstrates that increased model complexity (two-source models) does not inherently lead to superior performance over simpler one-source models, emphasizing the impact of parameter uncertainty.
• The research highlights the importance of considering irrigation, harvest, and oasis effects for accurate model application and parameter adjustment for specific field characteristics.
• It evaluates model applicability against FAO-recommended accuracy thresholds for various practical water management purposes.

Funding

Not explicitly stated in the paper. The authors acknowledge the Iran Water Research Institute for sharing scintillometer data and Dr. Bahman Yargholi from Iran Agricultural Engineering Research Institute for providing regional soil and irrigation data.

Citation

@article{Sima2026Preliminary,
  author = {Sima, Somayeh and Dehkordi, Iman Raissi and Taghikhani, Mohammadhosein and Karimi, Neamat},
  title = {Preliminary evaluation of remote sensing evapotranspiration models for field-scale agricultural water management in arid central Iran},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2025.110084},
  url = {https://doi.org/10.1016/j.agwat.2025.110084}
}