Estimating leaf chlorophyll content of shade grown tea based on PROSPECT model inversion

Abstract

Green tea-flavored sweets have become popular and then some techniques, such as shading treatments, have been developed for increasing chlorophyll content, which is important for improving tea leaf appearance. Chlorophyll content estimation is one of the most common applications of hyperspectral remote sensing, however, previous studies were based on measurements under relatively low stress conditions. The PROSPECT model is one of the most famous radiative transfer models and has been widely used for retrieving chlorophyll, carotenoid, or dry matter content. In this study, the performances of the three versions, which can estimate chlorophyll content from reflectance, were compared. Using PROSPECT-D, root mean square errors of 7.12μg/cm² was achieved and then it could be a strong tool for assessing the qualities of shade grown tea.