Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (T_wet), dry reference temperature (T_dry), and mean canopy temperature (T_l) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which T_wet was the mean of the lowest 5% of canopy temperatures and T_dry was the air temperature (T_air) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R² = 0.660) and tr (R² = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture.

Knowing plant water content (PWC) is of great significance for precision irrigation of field crop. The aim of this study is to monitor the PWC of cotton non-destructively in situ. A six-band multispectral camera embedded on an unmanned aerial vehicle (UAV) was used to collect images at flowering and boll-forming stages of the cotton. Thirteen vegetation indices (VI) were extracted from the camera. Consequently, all the VIs were fused with canopy temperature mathematically into vegetation supply water indices (VSWI). Unary and multivariate models were used to establish the relationship between VSWIs and the water content of leaf, petiole, stalk as well as bud & boll, respectively. Results indicated significant correlations (P < 0.01) between the VSWI from green index (VSWI_GI) and leaf water content (LWC), and between the VSWI from MERIS terrestrial chlorophyll index by the second infrared band (VSWI_MTCI2) and bud & boll water content (BWC). The correlation coefficients between the stalk water content (SWC) and VSWI_MTCI2 as well as VSWI_DATT2 were both −0.895. The best retrieval model of LWC, SWC, and BWC were the multivariate linear models for the much higher estimation ability. Coefficients of determination for modelling and validation were close to or greater than 0.8, and the root-mean-square errors (RMSE) for validation were less than 0.17, and the relative errors (RE) were less than 18%. The results showed all these models have relatively high accuracy and can provide a new method to efficiently monitor water content in cotton plants.