File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

TitleEstimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression
Authors
Issue Date2020
PublisherWiley-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, Epub How to Cite?
AbstractThe 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 Identifierhttp://hdl.handle.net/10722/280396
ISSN
2019 Impact Factor: 6.362
2015 SCImago Journal Rankings: 2.983

 

DC FieldValueLanguage
dc.contributor.authorFu, P-
dc.contributor.authorMeacham-Hensold, K-
dc.contributor.authorGuan, K-
dc.contributor.authorWu, J-
dc.contributor.authorBernacchi, C-
dc.date.accessioned2020-02-07T07:40:24Z-
dc.date.available2020-02-07T07:40:24Z-
dc.date.issued2020-
dc.identifier.citationPlant, Cell and Environment, 2020, Epub-
dc.identifier.issn0140-7791-
dc.identifier.urihttp://hdl.handle.net/10722/280396-
dc.description.abstractThe 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.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-3040-
dc.relation.ispartofPlant, Cell and Environment-
dc.rightsPostprint This is the peer reviewed version of the following article: [Plant, Cell and Environment, 2020], which has been published in final form at [http://dx.doi.org/10.1111/pce.13718]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleEstimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression-
dc.typeArticle-
dc.identifier.emailWu, J: jinwu@hku.hk-
dc.identifier.authorityWu, J=rp02509-
dc.description.naturepostprint-
dc.identifier.doi10.1111/pce.13718-
dc.identifier.hkuros309069-
dc.identifier.volumeEpub-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats