Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)

Abstract

Chlorophyll a+b (Ca+b) and carotenoids (Cx+c) are leaf pigments associated with photosynthesis, participation in light harvesting and energy transfer, quenching and photoprotection. This manuscript makes progress on developing methods for leaf carotenoid content estimation, using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Imagery was acquired over 3 years using two different UAV platforms, a 6-band multispectral camera and a micro-hyperspectral imager flown with 260 bands at 1.85 nm/pixel and 12-bit radiometric resolution, yielding 40 cm pixel size and a FWHM of 6.4 nm with a 25-μm slit in the 400–885 nm spectral region. Field data collections were conducted in August 2009–2011 in the western area of Ribera del Duero Appellation d’Origine, northern Spain. A total of twelve full production vineyards and two study plots per field were selected to ensure appropriate variability in leaf biochemistry and vine physiological conditions. Leaves were collected for destructive sampling and biochemical determination of chlorophyll a+b and carotenoids conducted in the laboratory. In addition to leaf sampling and biochemical determination, canopy structural parameters, such as grid size, number of vines within each plot, trunk height, plant height and width, and row orientation, were measured on each 10 m × 10 m plot. The R515/R570 index recently proposed for carotenoid estimation in conifer forest canopies was explored for vineyards in this study. The PROSPECT-5 leaf radiative transfer model, which simulates the carotenoid and chlorophyll content effects on leaf reflectance and transmittance, was linked to the SAILH and FLIGHT canopy-level radiative transfer models, as well as to simpler approximations based on infinite reflectance R∞ formulations. The objective was to simulate the pure vine reflectance without soil and shadow effects due to the high resolution hyperspectral imagery acquired from the UAV, which enabled targeting pure vines. The simulation results obtained with synthetic spectra demonstrated the effects due to Ca+b content on leaf Cx+c estimation when the R515/R570 index was used. Therefore, scaling up methods were proposed for leaf carotenoid content estimation based on the combined R515/R570 (sensitive to Cx+c) and TCARI/OSAVI (sensitive to Ca+b) narrow-band indices. Results demonstrated the feasibility of mapping leaf carotenoid concentration at the pure-vine level from high resolution hyperspectral imagery, yielding a root mean square error (RMSE) below 1.3 μg/cm2 and a relative RMSE (R-RMSE) of 14.4% (FLIGHT) and 12.9% (SAILH) for the 2 years of hyperspectral imagery. The simpler formulation based on the infinite reflectance model by Yamada and Fujimura yielded lower errors (RMSE = 0.87 μg/cm2; R-RMSE < 9.7%), although the slope deviated more from the 1:1 line. Maps showing the spatial variability of leaf carotenoid content were estimated using this methodology, which targeted pure vines without shadow and background effects.