Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems.

Vegetation indices (VIs) are widely used as efficient proxies for canopy structure and pigment dynamics, whereas solar-induced chlorophyll fluorescence (SIF) provides a direct indicator of photosynthetic activity. Accurate monitoring of these variables is essential for ecosystem assessment, agricultural management, and satellite product validation. However, long-term field spectroscopy is often influenced by instrument aging and environmental variability, altering radiometric calibration coefficients and introducing uncertainty. In this study, we systematically evaluated the impact of interannual calibration coefficients derived in 2023 and 2024 on six commonly used VIs and SIF. These variables were retrieved from ground-based hyperspectral observations acquired continuously using FLAME-T and QEPRO+ spectrometers over a full rice-growing season. Furthermore, MODIS-like broadband indices were simulated through spectral convolution with sensor response functions. Results revealed clear interannual differences in calibration coefficients, particularly in the blue (450–500 nm) and red-edge (700–800 nm) regions. PRI exhibited the highest sensitivity to calibration changes. SIF also showed pronounced sensitivity due to its intrinsically weak signal and strong channel dependence. Conversely, NDVI and ARVI were highly robust, with the calibration-induced bias of NDVI remaining near zero, benefiting from its normalized formulation and strong near-infrared reflectance. Broadband simulation modified the propagation of uncertainty in an index-dependent manner. Compared with narrowband counterparts, broadband EVI, SAVI, and PRI showed reduced sensitivity, whereas NDVI exhibited a slight sensitivity increase. These findings demonstrate a hierarchy of calibration sensitivity governed by signal strength, spectral band selection, and algorithm design. They emphasize the necessity of frequency-dependent calibration strategies for reliable products in satellite validation studies.