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Plant Science Today (PST)

Review Articles

Vol. 13 No. sp2 (2026): Recent Advances in Agriculture

Recent trends in crop water stress monitoring using remote sensing technologies: A review: Crop water stress assessment using remote sensing

DOI
https://doi.org/10.14719/pst.13057
Submitted
4 December 2025
Published
04-06-2026

Abstract

Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE) < 0.073) with accurate detection of crop water content at the 9th leaf and milking stages, as well as stomatal conductance at tasseling and silking stages. Combining UAV-derived spectral and textural features enhanced model accuracy in cotton, with extreme gradient boosting (XGBoost) showing the highest performance for canopy equivalent water thickness (CEWT) (R² = 0.75) and crop water stress index (CWSI) (R² = 0.90). Despite significant advances, problems remain in standardising data acquisition procedures, decreasing fluctuations due to environmental variability and combining UAV-derived data into reliable decision-support systems for precise irrigation management. The review emphasises key research gaps, such as the demand for strong cross-regional validation, real-time monitoring and the development of compatible systems linking UAV imagery data with ground sensors and crop models. Overall, advances in UAV-based remote sensing technology are altering crop water stress (CWS) detection, creating new potential to enhance agricultural resilience, boost resource-efficient management and focusing on increasing concerns with respect to climate-induced water stress.

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