Meza et al. (2026) High-resolution actual evapotranspiration in almond orchards by integrating physiological and TSEB models

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-10-14
• Authors: Karem Meza, Nicolas Bambach, Kyle R. Knipper, Mina Momayyezi, Andrew J. McElrone, Alfonso F. Torres‐Rua, Moises Rodrigo Duran-Gomez, Sebastian J. Castro, Lawrence E. Hipps, William P. Kustas
• DOI: 10.1016/j.agrformet.2026.111489

Research Groups

• Department of Viticulture and Enology, University of California, Davis, CA, USA
• Sustainable Agricultural Water Systems Research Unit, ARS, USDA, Davis, CA, USA
• Crops Pathology and Genetics Research, ARS, USDA, Davis, CA, USA
• Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA
• Department of Plants, Soils, and Climate, Utah State University, Logan, UT, USA
• Hydrology and Remote Sensing Lab, ARS, USDA, Beltsville, MD, USA

Short Summary

This study developed and applied a novel physiological-surface energy balance-based thermal infrared (TIR) temperature correction and a daily evapotranspiration (ETa) upscaling method to improve high-resolution ETa estimates in almond orchards. It demonstrated that integrating this correction with the Two-Source Energy Balance (TSEB) model significantly enhances the accuracy of instantaneous and daily ETa, supporting precision irrigation.

Objective

• To evaluate and integrate a novel physiological-surface energy balance-based thermal infrared (TIR) temperature correction method and a daily actual evapotranspiration (ETa) upscaling method into the Two-Source Energy Balance (TSEB) model at three almond orchards in California.
• To assess the performance of a coupled leaf-physiological and energy balance model against measurements of canopy temperature, stomatal conductance, and photosynthesis.
• To determine if Uncrewed Aerial System (UAS)-based surface energy flux estimates from TSEB-Priestley-Taylor (TSEB-PT) and TSEB-2T models, incorporating UAS-acquired TIR temperature correction, improve ETa accuracy.
• To investigate how different upscaling approaches improve the accuracy of daily ETa estimates when instantaneous latent heat flux (LE) is modeled from various UAS flight times.

Study Configuration

• Spatial Scale: Three commercial and mature almond orchards (WWF, OLA, VAC sites) in California. Leaf-level physiological measurements, tree-level canopy temperature modeling, field-scale UAS imagery (multispectral 10 cm x 10 cm, TIR 60 cm x 60 cm), and flux footprint grids (6.5 m).
• Temporal Scale: Growing seasons (March to November) from 2021 to 2024. Hourly (eddy covariance and model runs), instantaneous (UAS flights at pre-solar noon, solar noon, and mid-afternoon), and daily (upscaled ETa).

Methodology and Data

• Models used:
  • Two-Source Energy Balance (TSEB) model, including TSEB-Priestley-Taylor (TSEB-PT) and TSEB-2T (contextual approach).
  • Coupled physiological and energy balance model (integrating Farquhar–von Caemmerer–Berry (FvCB), Ball–Woodrow–Berry (BWB), and leaf energy balance models).
  • Flux Footprint Prediction (FFP) model (Kljun et al., 2015).
• Data sources:
  • Uncrewed Aerial System (UAS) imagery: Thermal infrared (TIR) and multispectral (Blue, Green, Red, Near-infrared, Red-edge) imagery collected using hybrid VTOL/fixed-wing and DJI Matrice 300 with Altum camera.
  • Eddy Covariance (EC) flux towers: Continuous measurements of sensible heat flux (H), latent heat flux (LE), soil heat flux (G), net radiation (Rn), air temperature (Ta), wind speed, relative humidity (RH), and barometric pressure.
  • In-situ physiological measurements: Net CO2 assimilation rate (An), stomatal conductance (gs), intercellular airspace CO2 concentration (Ci), and leaf temperature (Tl) using LI-COR 6800 system; Leaf Area Index (LAI) using LI-2200C Plant Canopy Analyzers.
  • Ground truth/calibration data: Infrared radiometers (IRTs), soil thermocouples, blackbody reference, AeroPoint ground control points (GCPs), and reference tarps for UAS TIR calibration and correction.
  • Satellite imagery: Sentinel-2 (COPERNICUS-S2-SR-HARMONIZED) for LAI estimates in temporal modeling.

Main Results

• The coupled physiological and surface energy balance model accurately predicted leaf temperature (Tl) in good agreement with observed canopy temperature (Tc), with R² values ranging from 0.92 to 0.98 and Root Mean Square Error (RMSE) from 0.84 to 1.6 °C. It also showed reasonable agreement for net CO2 assimilation rate (An) (R²=0.96, RMSE=2.57 µmol m⁻² s⁻¹) and stomatal conductance (gs) (R²=0.65, RMSE=0.05 mol m⁻² s⁻¹).
• Applying the physiological-surface energy balance-based TIR temperature correction to UAS imagery improved LE estimates, reducing the Mean Absolute Error (MAE) by 18.3%.
• The TSEB-2T model, which directly ingests corrected canopy (Tc) and soil (Ts) temperatures, consistently outperformed the TSEB-PT model for estimating surface energy fluxes.
• Upscaling methods performed best when using instantaneous LE values between 11:00 and 13:00 Pacific Daylight Time (PDT), with RMSE ranging from 0.02 to 0.65 mm day⁻¹ based on EC measurements.
• The TSEB-2TUAS-CMT scenario (using coupled physiological model and thermocouples for Tc/Ts correction) combined with the solar radiation (Rs) upscaling method yielded the most accurate daily ETa estimates, with an R² of 0.83 and an MAE of 0.54 mm day⁻¹. This configuration reduced RMSE by approximately 40% compared to using uncorrected TIR imagery.
• Under advective conditions, particularly at the OLA and VAC sites, the TSEB-PT and TSEB-2T models tended to underestimate daily ETa, highlighting a limitation in capturing lateral heat and moisture transport.

Contributions

• Developed and integrated a novel physiological-surface energy balance-based method for correcting UAS-acquired thermal infrared (TIR) canopy temperature, significantly improving the accuracy of ETa estimates.
• Demonstrated the superior performance of the TSEB-2T model (which uses separate canopy and soil temperatures) over the TSEB-PT model for high-resolution ETa estimation in almond orchards.
• Evaluated and optimized daily ETa upscaling methods, identifying optimal UAS flight times (11:00 to 13:00 PDT) for accurate daily ETa estimation.
• Provided high-spatial-resolution daily ETa maps, offering valuable information for precision irrigation management and supporting the validation of satellite-derived ET products like OpenET.
• Highlighted the limitations of current TSEB models in accurately estimating ETa under strong advective conditions and suggested future research directions.

Funding

• Tree Crop Remote Sensing of Evapotranspiration Experiment (T-REX) project.
• Almond Board of California [project WATER16].
• U.S. Department of Agriculture (USDA) Agricultural Marketing Service.

Citation

@article{Meza2026Highresolution,
  author = {Meza, Karem and Bambach, Nicolas and Knipper, Kyle  R. and Momayyezi, Mina and McElrone, Andrew J. and Torres‐Rua, Alfonso F. and Duran-Gomez, Moises Rodrigo and Castro, Sebastian J. and Hipps, Lawrence E. and Kustas, William P.},
  title = {High-resolution actual evapotranspiration in almond orchards by integrating physiological and TSEB models},
  journal = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111489},
  url = {https://doi.org/10.1016/j.agrformet.2026.111489}
}