Estimating photosynthetic traits in tea chrysanthemum using high-throughput leaf hyperspectral reflectance.

Hyperspectral remote sensing (RS) has demonstrated to be useful for estimating vegetation photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and the electron transport rate (Jmax). However, the spectral ranges used by RS models for predicting photosynthetic traits vary, and their predictive performance across different crop varieties is poor. This study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability.