|FU, PENG - University Of Illinois|
|MEACHAM-HENSOLD, KATHERINE - University Of Illinois|
|GUAN, KAIYU - University Of Illinois|
|WU, JIN - University Of Hong Kong|
Submitted to: Plant Cell and Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/3/2020
Publication Date: 1/20/2020
Citation: Fu, P., Meacham-Hensold, K., Guan, K., Wu, J., Bernacchi, C.J. 2020. Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression. Plant Cell and Environment. 43(5):1241-1258. https://doi.org/10.1111/pce.13718.
Interpretive Summary: Photosynthesis is the process in which plants use sunlight, water, and carbon dioxide to make sugars. These sugars then lead to the growth of the plant and, for crops, ultimately leads to harvested yields. Crop with higher photosynthesis generally have higher growth and higher yields. But, photosynthesis is difficult to measure. In this experiment, we developed a method to use images of crop fields as a means to measure photosynthesis. Unlike standard digital cameras that have only three measurement colors (red, green, and blue), the images we collected measured hundreds of colors, some of which had information related to photosynthesis capabilities of the plants. We showed that the new technique using hundreds of colors did a good job of measuring photosynthesis compared with the old, much slower and difficult technique. These results can help to improve our ability to breed better crops by making more measurements throughout the life of the plant.
Technical 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., spectral indices, numerical model inversion, and 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 numerically inversed crop traits. Compared to PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting Vcmax (R2 = 0.84 ± 0.02) while a similar performance for Jmax (R2 = 0.80 ± 0.03). Further analysis on spectral resampling revealed that Vcmax and Jmax could be predicted with ~10 spectral bands and with spectral resolution less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.