Biangular-Combined Vegetation Indices to Improve the Estimation of Canopy Chlorophyll Content in Wheat Using Multi-Angle Experimental and Simulated Spectral Data

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 (R-2) between mono-angular VIs and CCC, at back-scattering directions, were mostly higher than those at forward-scattering directions. Mono-angular VIs at +30 degrees angle, were closest to the hot-spot position in our case, achieved the highest R-2 among 13 viewing angles including the nadir observation. The general formulation for the newly developed BCVIs was BCVIVI = f x VI(theta 1) - (1 - f) x VI(theta 2), in which the VI was used to characterize chlorophyll status, while the subtraction of VI at theta 1 and theta 2 viewing angles in a proportion was used to highlight the canopy structural information. From our result, the values of the theta 1 and theta 2 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 the experimental data, indicated by the increase in R-2 by 25.1-51.4%, as compared to the corresponding mono-angular VIs at +30 degrees angle. The BCVIMCARI[705,750] was proved to greatly undermine the saturation effect of mono-angular MCARI[705,750], expressing the best linearity and the most sensitive to CCC, with R-2 of 0.98 and 0.72 for simulated and experimental data, respectively. Our study will eventually have extensive prospects in monitoring crop phenotype dynamics in for example large breeding trials.

Automated summary

Canopy chlorophyll content (CCC) is crucial for crop growth and yield improvement. Conventional nadir reflectance lacks expression on geometric structures and shaded areas of vegetation canopy, leading to saturation of vegetation indices (VIs) at high CCC levels. In this study, multi-angle reflectance data was used to estimate CCC and a novel algorithm for biangular-combined vegetation indices (BCVIs) was proposed. The results showed that back-scattering directions had higher spectral reflectance values and coefficients of determination (R-2) between mono-angular VIs and CCC compared to forward-scattering directions. The BCVIs significantly improved CCC modeling, especially for experimental data, with an increase in R-2 of 25.1-51.4% compared to mono-angular VIs at +30 degrees angle. BCVIMCARI[705,750] showed the best linearity and sensitivity to CCC, with R-2 of 0.98 and 0.72 for simulated and experimental data, respectively. This study has extensive prospects in monitoring crop phenotype dynamics, such as large breeding trials.