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
Keywordsearth system models
global carbon cycles
high-throughput mapping
hyperspectral imaging
machine learning
photosynthesis
plant breeding
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, v. 43 n. 5, p. 1241-1258 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
2023 Impact Factor: 6.0
2023 SCImago Journal Rankings: 2.030
ISI Accession Number ID

 

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, v. 43 n. 5, p. 1241-1258-
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.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectearth system models-
dc.subjectglobal carbon cycles-
dc.subjecthigh-throughput mapping-
dc.subjecthyperspectral imaging-
dc.subjectmachine learning-
dc.subjectphotosynthesis-
dc.subjectplant breeding-
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.scopuseid_2-s2.0-85080061005-
dc.identifier.hkuros309069-
dc.identifier.volume43-
dc.identifier.issue5-
dc.identifier.spage1241-
dc.identifier.epage1258-
dc.identifier.isiWOS:000516585700001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0140-7791-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats