Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

Abstract

The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, i.e., reflectance spectra‐, spectral indices‐, and numerical model inversions‐based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for eleven tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded a R2 of ~0.8 for predicting Vcmax and Jmax, higher than a R2 of ~0.6 provided by PLSR of numerical inversions. Compared to PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting Vcmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2