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, 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.
Highlights
Photosynthetic traits of vegetation canopies are important parameters of process-based Earth system models to understand global carbon cycles (Croft et al, 2017; Rogers, 2014; Schaefer et al, 2012).Peng Fu and Katherine Meacham-Hensold should be considered joint first authors.the lack of spatially and temporally continuous information on photosynthetic traits for these Earth system models results in a large uncertainty to account for carbon sinks, sources, and exchange between the atmosphere and the terrestrial biosphere (Rogers, 2014)
Þ2 pffiffiffiffiffiffiffiffiffi full width at half maximum (FWHM) = 2 2ln2δ where Rconv refers to resampled reflectance spectra, R0 refers to original reflectance spectra, Wλ is the weight per wavelength derived from 4 | RESULTS
These findings suggested that there existed an optimal number of spectral bands (132) used for predicting Vcmax and more than or less than this number of spectral bands could reduce the predictive performance of partial least square regression (PLSR)
Summary
Photosynthetic traits of vegetation canopies are important parameters of process-based Earth system models to understand global carbon cycles (Croft et al, 2017; Rogers, 2014; Schaefer et al, 2012). It consists of image-preprocessing and further modelling of canopy-level reflectance with photosynthetic variables Vcmax and Jmax through PLSR and indices-based analysis. The optimal number of latent variables was determined using the lowest RMSE of prediction from cross validations, following Esbensen et al (2002), to prevent overfitting These spectral indices were generally designed for estimating photosynthetic pigment contents (e.g., chlorophyll a) and structure characteristics at leaf and canopy levels and may have close associations with photosynthetic capacity (e.g., Croft et al, 2017). The original reflectance spectra were resampled at different spectral resolutions to investigate the impacts of spectral regions on the PLSR performance for predicting photosynthetic capacities The objective of this analysis is to provide insights about whether a hyperspectral camera can be replaced by a multispectral camera to quantify photosynthetic traits in a high-throughput manner. : ð5Þ where Rconv refers to resampled reflectance spectra, R0 refers to original reflectance spectra, Wλ is the weight per wavelength derived from
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