Very-High-Resolution, Multi-Season Monitoring of Crop Evapotranspiration and Water Stress with UAV Data and TSEB Integration

Abstract

Abstract. Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (fg). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic fg improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.

Plant phenotyping relevance

UAVの熱・マルチスペクトル・LiDARデータとTSEB-PTモデルにより、作物の蒸発散と水ストレスを推定する手法を中心に扱い、渦相関データで技術検証しているため、植物フェノタイピング手法研究に該当する。

Code and data availability

The supplied blocks describe UAV TIR/MS/LiDAR phenotyping and TSEB-PT modeling, but contain no author-deposited dataset, code, model, or supplement with an explicit public availability statement. The only URL besides the preprint DOI is the general TERENO data portal for the DE-RuS observatory, which is a generic site/

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