ArticleFrontiers in plant science2022
Biangular-Combined Vegetation Indices to Improve the Estimation of Canopy Chlorophyll Content in Wheat Using Multi-Angle Experimental and Simulated Spectral Data.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 10 citations in OpenAlex.
- Wheat growth parameters prediction based on dual output Bayesian neural network using multi-modal information.Frontiers in plant science · 2026Article
- Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods.Frontiers in plant science · 2025Article
- Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.Plant phenomics (Washington, D.C.) · 2023Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Canopy chlorophyll content (CCC) indicates the photosynthetic functioning of a crop, which is essential for the growth and development and yield increasing. Accurate estimation of CCC from remote-sensing data benefits from including information on leaf chlorophyll and canopy structures. However, conventional nadir reflectance is usually subject to the lack of an adequate expression on the geometric structures and shaded parts of vegetation canopy, and the derived vegetation indices (VIs) are prone to be saturated at high CCC level. Using 3-year field experiments with different wheat cultivars, leaf colors, structural types, and growth stages, and integrated with PROSPECT+SAILh model simulation, we studied the potential of multi-angle reflectance data for the improved estimation of CCC. The characteristics of angular anisotropy in spectral reflectance were investigated. Analyses based on both simulated and experimental multi-angle hyperspectral data were carried out to compare performances of 20 existing VIs at different viewing angles, and to propose an algorithm to develop novel biangular-combined vegetation indices (BCVIs) for tracking CCC dynamics in wheat. The results indicated that spectral reflectance values, as well as the coefficient of determination (
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