Synchronized UAV multi-angle inversion of canopy structure parameters in wheat breeding materials

Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R ² > 0.80, RRMSE $<0.2$ ) and exhibited anisotropy consistent with ground measurements. The comparative analysis demonstrated that multi-angle observations significantly improved retrieval accuracy for LAI (R ² = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa (R ² = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy (R ² ≥ 0.73), with MA models providing marginal gains (R ² = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.