Biangular-Combined Vegetation Indices to Improve the Estimation of Canopy Chlorophyll Content in Wheat Using Multi-Angle Experimental and Simulated Spectral Data - Kong, Weiping; Huang, Wenjiang; Ma, Lingling; Li, Chuanrong; Tang, Lingli; Guo, Jiawei; Zhou, Xianfeng; Casa, Raffaele (Frontiers in Plant Science, 2022)
Canopy chlorophyll content (CCC) indicates the photosynthetic functioning of crop, which is essential for the growth 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 adequate expression on the geometric structures and shaded parts of canopy, and the derived vegetation indices (VIs) are prone to be saturated at high CCC level. Using thousands of PROSPECT+SAILh model simulation, integrated with three-year field experiments, we studied the potential of multi-angle reflectance data for the improved estimation of CCC. Analyses based on both simulated and experimental datasets 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. Results indicated that spectral reflectance values, as well as the coefficient of determination (R2) between mono-angular VIs and CCC, at back-scattering directions were mostly higher than those at forward-scattering directions. Mono-angular VIs at +30°, where closest to the hot-spot position in our case, achieved the highest R2 among 13 viewing angles including the nadir observation. The general formulation for BCVIs was BCVI=f×VI(θ1)-(1-f)×VI(θ2), in which the VI was used to characterize chlorophyll status, while the subtraction of VI at θ1 and θ2 angles in a proportion was used to highlight canopy structural information. From our result, the values of the θ1 and θ2 around hot-spot and dark-spot positions and the f of 0.6 or 0.7 were found as the optimized values. Through comparisons revealed that large improvements on CCC modeling could be obtained by the BCVIs, especially for experimental data, indicated by the increase in R2 by 25.1% to 51.4% as compared to corresponding mono-angular VIs at +30° angle. The BCVIMCARI[705,750] was proved to greatly undermine the saturation effect of mono-angular MCARI[705,750], expressing the best linearity and most sensitive to CCC, with R2 of 0.98 and 0.72 for simulated and experimental data respectively. Our study will eventually have extensive prospect in monitoring crop phenotype dynamics in for example large breeding trials.