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.

Canopy temperature (T_c) is an important indicator for characterizing crop water status and serves as the core variable for constructing the Crop Water Stress Index (CWSI). Timely and accurate diagnosis of crop water stress is of great significance for precision irrigation and yield improvement. Owing to its non-contact and high-efficiency characteristics, unmanned aerial vehicle (UAV) remote sensing has become an effective approach for high-spatiotemporal-resolution monitoring of crop water conditions. However, variations in observation geometry can introduce thermal directional effects in canopy temperature, thereby reducing the stability and reliability of CWSI estimation. In this study, multi-angular thermal infrared imagery acquired by a UAV platform was utilized to investigate the directional characteristics of winter wheat canopy temperature. A kernel-driven model was employed to separate the directional components of canopy temperature and retrieve isotropic temperature parameters that more closely represent the actual thermal status of the crop canopy. Based on these temperature parameters, three CWSI models were constructed and evaluated for crop water stress diagnosis. The results demonstrated that (1) winter wheat canopy temperature exhibited pronounced directional characteristics, and the observed temperature generally decreased with increasing relative azimuth angle between the viewing direction and solar incident direction; (2) after angular correction, the isotropic canopy temperature simulated by the kernel-driven model showed an improved correlation with soil moisture content at a depth of 30 cm (R² = 0.54); and (3) when angular-corrected canopy temperature was used as the input variable for different CWSI models, the sensitivity of all models to crop water variation was substantially enhanced, resulting in improved discrimination among different irrigation treatments. Among the evaluated approaches, the empirical CWSI model achieved the best performance in diagnosing crop water stress variations (R² = 0.73, RMSE = 1.59%). These findings provide a theoretical basis for UAV-based thermal infrared remote sensing of crop water status and offer technical support for precision irrigation management.

Soil moisture content (SMC) is critical for crop growth and precision irrigation. Multi-angular remote sensing can capture directional reflectance variations that may provide additional information related to crop water status. However, the potential of hotspot-sensitive multi-angular observations for root-zone SMC estimation remains insufficiently explored, particularly in winter wheat. This study evaluated the potential of hotspot-sensitive multi-angular observations for root-zone SMC estimation using UAV observations and the LESS (LargE-Scale remote sensing data and image Simulation framework) radiative transfer model. Multispectral imagery of winter wheat was acquired under four irrigation treatments (T1–T4). The LESS model was parameterized using field-measured canopy characteristics to simulate bidirectional reflectance factors (BRFs) across the observation hemisphere. The simulated and observed datasets were used to analyze hotspot dynamics and evaluate the contribution of hotspot-sensitive observations to SMC estimation. Among the investigated bands, hotspot characteristics at 560 nm exhibited the strongest response to water stress. LESS-simulated BRFs showed greater angular variability (CV ≈ 0.30) than UAV-observed BRFs (CV ≈ 0.18). The highest SMC estimation performance was obtained in hotspot-sensitive backscattering directions, with an R² ≈ 0.60 and an RMSE ≈ 0.022 m³ m⁻³. These findings indicate that hotspot-sensitive multi-angular observations provide useful complementary information for SMC estimation.