@article{Mi2026, 
author = {Zhiwen Mi and Jinya Su and Qifan Chen and Bingyou Ding and Tengfei Han and Qifan Wang and Yiyan Fan and Dejun Han and Jianhui Wu and Fahu Xu and Baofeng Su},
title = {Synchronized UAV multi-angle inversion of canopy structure parameters in wheat breeding materials},
year = {2026},
journal = {Plant Phenomics},
volume = {8},
number = {2},
pages = {100183},
keywords = {Unmanned aerial vehicle (UAV), Semi-empirical BRDF model, Reflectance anisotropy, Leaf inclination distribution parameter, Leaf area index, Fractional vegetation cover},
url = {https://www.sciopen.com/article/10.1016/j.plaphe.2026.100183},
doi = {10.1016/j.plaphe.2026.100183},
abstract = {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 R2 > 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 (R2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa (R2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy (R2 ≥ 0.73), with MA models providing marginal gains (R2 = 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.}
}