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Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression
Author(s) -
Fu Peng,
MeachamHensold Katherine,
Guan Kaiyu,
Wu Jin,
Bernacchi Carl
Publication year - 2020
Publication title -
plant, cell and environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 200
eISSN - 1365-3040
pISSN - 0140-7791
DOI - 10.1111/pce.13718
Subject(s) - hyperspectral imaging , partial least squares regression , mean squared error , canopy , spectral line , photosynthesis , spectral bands , remote sensing , mathematics , resampling , inversion (geology) , reflectivity , environmental science , biological system , statistics , biology , botany , physics , optics , geology , paleontology , structural basin , astronomy
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 μmol m −2 s −1 ) while a similar performance for J max ( R 2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 μmol 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.