Journal
PLANT CELL AND ENVIRONMENT
Volume 43, Issue 5, Pages 1241-1258Publisher
WILEY
DOI: 10.1111/pce.13718
Keywords
earth system models; global carbon cycles; high-throughput mapping; hyperspectral imaging; machine learning; photosynthesis; plant breeding
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Funding
- Bill and Melinda Gates Foundation [OPP1172157]
- Department for International Development
- Bill and Melinda Gates Foundation [OPP1172157] Funding Source: Bill and Melinda Gates Foundation
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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.
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