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

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)

Code and data availability

This is a review article on UAV-based crop water stress monitoring. It summarizes prior studies' results (R², RMSE values) and describes generic workflows, but contains no paper-specific public phenotype datasets, imagery, code, models, or supplements with availability statements. All cited DOIs are prior work, not the

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