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

Abstract

<p indent=0mm>Estimating the ratio between carotenoid to chlorophyll <italic>a</italic> (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 <italic>a</italic> 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 (<italic>R</italic>²_{leaf level} = 0.69 and <italic>R</italic>²_{canopy level} = 0.67) and Car concentration (<italic>R</italic>²_{leaf level} = 0.44 and<italic> R</italic>²_{canopy level} = 0.36). The best estimation of the Car/Chla ratio and Car was provided by PLSR models with <italic>R</italic>² > 0.80 between the estimated and measured value for Car/Chla ratio and <italic>R</italic>² <italic>= </italic>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 (<italic>R</italic>²_{leaf level} = 0.87 and <italic>R</italic>²_{canopy level} = 0.84) and Car (<italic>R</italic>²_{leaf level} = 0.73 and<italic> R</italic>²_{canopy level} = 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.