Estimation of cotton Car/Chla ratio by hyperspectral vegetation indices and partial least square regression

Abstract

Estimating the ratio between carotenoid to chlorophyll a (Car/Chla) provides an additional avenue for the assessment of physiology and phenology of plant growth and development. With the aim of assessing cotton Car/Chla ratio from hyperspectral reflectance, a wide range of carotenoid (Car) and chlorophyll a concentrations, and leaf and canopy reflectance at cotton different growth stages were measured. The performance of a variety of Car/Chla ratio related vegetation indices and partial least square regression (PLSR) for Car/Chla ratio and Car estimation were tested. Among all tested vegetation indices, PRI (Photochemical Reflectance Index) and linear PRI models had the most significant correlations with Car/Chla ratio and Car, and could accurately estimate, Car/Chla ratio (Rleaf level2 = 0.69 and Rcanopy level2 = 0.67) and Car concentration (Rleaf level2 = 0.44 and Rcanopy level2 = 0.36). The best estimation of the Car/Chla ratio and Car was provided by PLSR models with R2 > 0.80 between the estimated and measured value for Car/Chla ratio and R2 = 0.74 for Car. Both reflectance indices and PLSR method were more successful for the estimation of Car/Chla ratio than for that of Car concentration, indicating the promising potential of Car/Chla ratio as a powerful indicator using for plant status monitoring by remote sensing. Besides, accuracy test of models using validation dataset highlighted the remarkable performance of PLSR for Car/Chla (Rleaf level2 = 0.87 and Rcanopy level2 = 0.84) and Car (Rleaf level2 = 0.73 and Rcanopy level2 = 0.74) estimated by hyperspectral reflectance at both the leaf and canopy levels. The results further prove the remarkable performance of hyperspectral reflectance for the estimation of Car/Chla ratio, and enrich the parameters for monitoring high temperature stress, water deficit stress, and nutrient stress and pest diseases by remote sensing in cotton.