Tracking crop evapotranspiration and water stress across seasons using multi-sensor UAV observations
- 1Forschungszentrum Jülich (Jülich, Germany)
Abstract
Accurate estimation of crop evapotranspiration (ET) requires resolving how canopy structure and physiological activity regulate land–atmosphere energy exchange. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model using very-high-resolution UAV multi-sensor observations (∼ 15 cm) to estimate ET over a 10 ha field across three seasons (sugar beet, potato, winter wheat). Thermal infrared data provided land surface temperature (LST), while multispectral (MS) and LiDAR data were used to characterize canopy properties, including a fusion-based estimate of the fraction of LAI that is green ( f g ) from MS-derived green area index (GAI) and LiDAR-derived plant area index (PAI). Results showed strong sensitivity to LST, highlighting the importance of thermal calibration, and demonstrated that while GAI and PAI performed similarly during peak growth, GAI better captured functional decline during stress and senescence, particularly in dense canopies. Incorporating dynamic f g improved ET estimates across crops and conditions. Modeled instantaneous ET showed good agreement with eddy covariance (EC) measurements, with RMSE ranging from 0.08 mm h ⁻¹ to 0.31 mm h ⁻¹ and R ² values from 0.28 to 0.97 (highest performance excluding senescence). High-resolution UAV-derived LST data and TSEB-PT modeling also enabled early detection of spatial stress patterns not captured by UAV structural or spectral metrics alone. Upscaling instantaneous ET using a reference ET fraction approach produced daily ET estimates with RMSE ranging from 0.96 to 2.30 mm d ⁻¹ and R ² ranging from 0.27 to 0.80 across crops, with improved consistency under energy-balance–closed EC conditions. These daily estimates also enabled assessment of potential crop water stress when compared relative to atmospheric demand (ET RAD = ET / ET ₀ ) and improved delineation of sub-field variability compared to coarser-resolution methods. These findings demonstrate the value of integrating UAV multi-sensor data with TSEB-PT for robust, high-resolution ET monitoring and practical applications in precision irrigation and water management.
Versions
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BibTeX
@article{bates2026tracking,
title = {{Tracking crop evapotranspiration and water stress across seasons using multi-sensor UAV observations}},
author = {Jordan Steven Bates and Carsten Montzka and Harry Vereecken and François Jonard},
journal = {Biogeosciences},
year = {2026},
month = oct,
doi = {10.5194/bg-23-7253-2026},
url = {https://bg.copernicus.org/articles/23/7253/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Tracking crop evapotranspiration and water stress across seasons using multi-sensor UAV observations
AU - Jordan Steven Bates
AU - Carsten Montzka
AU - Harry Vereecken
AU - François Jonard
PY - 2026
DA - 2026/10/16
JO - Biogeosciences
DO - 10.5194/bg-23-7253-2026
UR - https://bg.copernicus.org/articles/23/7253/2026/
AB - Accurate estimation of crop evapotranspiration (ET) requires resolving how canopy structure and physiological activity regulate land–atmosphere energy exchange. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model using very-high-resolution UAV multi-sensor observations (∼ 15 cm) to estimate ET over a 10 ha field across three seasons (sugar beet, potato, winter wheat). Thermal infrared data provided land surface temperature (LST), while multispectral (MS) and LiDAR data were used to characterize canopy properties, including a fusion-based estimate of the fraction of LAI that is green ( f g ) from MS-derived green area index (GAI) and LiDAR-derived plant area index (PAI). Results showed strong sensitivity to LST, highlighting the importance of thermal calibration, and demonstrated that while GAI and PAI performed similarly during peak growth, GAI better captured functional decline during stress and senescence, particularly in dense canopies. Incorporating dynamic f g improved ET estimates across crops and conditions. Modeled instantaneous ET showed good agreement with eddy covariance (EC) measurements, with RMSE ranging from 0.08 mm h ⁻¹ to 0.31 mm h ⁻¹ and R ² values from 0.28 to 0.97 (highest performance excluding senescence). High-resolution UAV-derived LST data and TSEB-PT modeling also enabled early detection of spatial stress patterns not captured by UAV structural or spectral metrics alone. Upscaling instantaneous ET using a reference ET fraction approach produced daily ET estimates with RMSE ranging from 0.96 to 2.30 mm d ⁻¹ and R ² ranging from 0.27 to 0.80 across crops, with improved consistency under energy-balance–closed EC conditions. These daily estimates also enabled assessment of potential crop water stress when compared relative to atmospheric demand (ET RAD = ET / ET ₀ ) and improved delineation of sub-field variability compared to coarser-resolution methods. These findings demonstrate the value of integrating UAV multi-sensor data with TSEB-PT for robust, high-resolution ET monitoring and practical applications in precision irrigation and water management.
SN - 1726-4189
ER -