File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: From the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance

TitleFrom the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance
Authors
Keywordsleaf mass area
partial least-squares regression (PLSR)
plant traits
remote sensing
specific leaf area
spectroscopy
Issue Date2019
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at https://nph-onlinelibrary-wiley-com.eproxy.lib.hku.hk/journal/14698137
Citation
New Phytologist, 2019, v. 224 n. 4, p. 1557-1568 How to Cite?
AbstractLeaf‐mass‐per‐area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 g m−2 to 393 g m−2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needle‐leaf species, and upper and lower canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age, and canopy position, from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2=0.89; RMSE=15.45 g m−2) Our finding highlights that the leaf economic spectrum is mirrored by the leaf optical spectrum paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.
Persistent Identifierhttp://hdl.handle.net/10722/276030
ISSN
2021 Impact Factor: 10.323
2020 SCImago Journal Rankings: 3.742
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSerbin, SP-
dc.contributor.authorWu, J-
dc.contributor.authorEly, K-
dc.contributor.authorKruger, EL-
dc.contributor.authorTownsend, PA-
dc.contributor.authorMeng, R-
dc.contributor.authorWolfe, BT-
dc.contributor.authorChlus, A-
dc.contributor.authorWang, Z-
dc.contributor.authorRogers, A-
dc.date.accessioned2019-09-10T02:54:30Z-
dc.date.available2019-09-10T02:54:30Z-
dc.date.issued2019-
dc.identifier.citationNew Phytologist, 2019, v. 224 n. 4, p. 1557-1568-
dc.identifier.issn0028-646X-
dc.identifier.urihttp://hdl.handle.net/10722/276030-
dc.description.abstractLeaf‐mass‐per‐area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 g m−2 to 393 g m−2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needle‐leaf species, and upper and lower canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age, and canopy position, from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2=0.89; RMSE=15.45 g m−2) Our finding highlights that the leaf economic spectrum is mirrored by the leaf optical spectrum paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at https://nph-onlinelibrary-wiley-com.eproxy.lib.hku.hk/journal/14698137-
dc.relation.ispartofNew Phytologist-
dc.rightsThis is the peer reviewed version of the following article: New Phytologist, 2019, v. 224 n. 4, p. 1557-1568, which has been published in final form at https://doi.org/10.1111/nph.16123. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectleaf mass area-
dc.subjectpartial least-squares regression (PLSR)-
dc.subjectplant traits-
dc.subjectremote sensing-
dc.subjectspecific leaf area-
dc.subjectspectroscopy-
dc.titleFrom the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance-
dc.typeArticle-
dc.identifier.emailWu, J: jinwu@hku.hk-
dc.identifier.authorityWu, J=rp02509-
dc.description.naturepostprint-
dc.identifier.doi10.1111/nph.16123-
dc.identifier.scopuseid_2-s2.0-85073966982-
dc.identifier.hkuros302692-
dc.identifier.volume224-
dc.identifier.issue4-
dc.identifier.spage1557-
dc.identifier.epage1568-
dc.identifier.isiWOS:000486883100001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0028-646X-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats