Articles | Volume 23, issue 20
https://doi.org/10.5194/bg-23-7253-2026
https://doi.org/10.5194/bg-23-7253-2026
Research article
 | 
16 Oct 2026
Research article |  | 16 Oct 2026

Tracking crop evapotranspiration and water stress across seasons using multi-sensor UAV observations

Jordan Steven Bates, Carsten Montzka, Harry Vereecken, and François Jonard

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Cited articles

Aboutalebi, M., Torres-Rua, A. F., McKee, M., Nieto, H., Kustas, W., and Coopmans, C.: The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T), P. Soc. Photo.-Opt. Ins., 11008, https://doi.org/10.1117/12.2519685, 2019. 
Acharya, B. and Sharma, V.: Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region, Remote Sensing, 13, https://doi.org/10.3390/rs13091822, 2021. 
Ali, M., Montzka, C., Stadler, A., Menz, G., Thonfeld, F., and Vereecken, H.: Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany), Remote Sensing, 7, 2808–2831, https://doi.org/10.3390/rs70302808, 2015. 
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome, https://www.fao.org/4/X0490E/X0490E00.htm (last access: 15 March 2026), 1998. 
Amani, S. and Shafizadeh-Moghadam, H.: A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data, Agr. Water Manage., 284, 108324, https://doi.org/10.1016/j.agwat.2023.108324, 2023. 
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Short summary
We used drones with thermal, multispectral, and laser sensors to measure crop water use across three growing seasons. Combining temperature with information on both green and structural plant growth improved estimates of crop water use, particularly as crops aged or experienced stress. The approach revealed detailed patterns of reduced water use across fields, supporting earlier identification of crop stress.
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