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Article: Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression
Title | Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression |
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Authors | |
Keywords | earth system models global carbon cycles high-throughput mapping hyperspectral imaging machine learning photosynthesis plant breeding |
Issue Date | 2020 |
Publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-3040 |
Citation | Plant, Cell and Environment, 2020, v. 43 n. 5, p. 1241-1258 How to Cite? |
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., reflectance spectra‐, spectral indices‐, and numerical model inversions‐based 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 numerical inversions. Compared to PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting Vcmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 |
Persistent Identifier | http://hdl.handle.net/10722/280396 |
ISSN | 2023 Impact Factor: 6.0 2023 SCImago Journal Rankings: 2.030 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fu, P | - |
dc.contributor.author | Meacham-Hensold, K | - |
dc.contributor.author | Guan, K | - |
dc.contributor.author | Wu, J | - |
dc.contributor.author | Bernacchi, C | - |
dc.date.accessioned | 2020-02-07T07:40:24Z | - |
dc.date.available | 2020-02-07T07:40:24Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Plant, Cell and Environment, 2020, v. 43 n. 5, p. 1241-1258 | - |
dc.identifier.issn | 0140-7791 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280396 | - |
dc.description.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., reflectance spectra‐, spectral indices‐, and numerical model inversions‐based 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 numerical inversions. Compared to PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting Vcmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 | - |
dc.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-3040 | - |
dc.relation.ispartof | Plant, Cell and Environment | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | earth system models | - |
dc.subject | global carbon cycles | - |
dc.subject | high-throughput mapping | - |
dc.subject | hyperspectral imaging | - |
dc.subject | machine learning | - |
dc.subject | photosynthesis | - |
dc.subject | plant breeding | - |
dc.title | Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression | - |
dc.type | Article | - |
dc.identifier.email | Wu, J: jinwu@hku.hk | - |
dc.identifier.authority | Wu, J=rp02509 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1111/pce.13718 | - |
dc.identifier.scopus | eid_2-s2.0-85080061005 | - |
dc.identifier.hkuros | 309069 | - |
dc.identifier.volume | 43 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1241 | - |
dc.identifier.epage | 1258 | - |
dc.identifier.isi | WOS:000516585700001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0140-7791 | - |