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

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, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R-2 of 0.8 for predicting V (cmax) and J (max), higher than an R-2 of 0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V (cmax) (R-2 = 0.84 +/- 0.02, RMSE = 33.8 +/- 2.2 mu mol m(-2) s(-1)) while a similar performance for J (max) (R-2 = 0.80 +/- 0.03, RMSE = 22.6 +/- 1.6 mu mol m(-2) s(-1)). Further analysis on spectral resampling revealed that V (cmax) and J (max) could be predicted with 10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.