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Article: From the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance
Title | From the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance |
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Authors | |
Keywords | leaf mass area partial least-squares regression (PLSR) plant traits remote sensing specific leaf area spectroscopy |
Issue Date | 2019 |
Publisher | Wiley-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? |
Abstract | Leaf‐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 Identifier | http://hdl.handle.net/10722/276030 |
ISSN | 2023 Impact Factor: 8.3 2023 SCImago Journal Rankings: 3.007 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Serbin, SP | - |
dc.contributor.author | Wu, J | - |
dc.contributor.author | Ely, K | - |
dc.contributor.author | Kruger, EL | - |
dc.contributor.author | Townsend, PA | - |
dc.contributor.author | Meng, R | - |
dc.contributor.author | Wolfe, BT | - |
dc.contributor.author | Chlus, A | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Rogers, A | - |
dc.date.accessioned | 2019-09-10T02:54:30Z | - |
dc.date.available | 2019-09-10T02:54:30Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | New Phytologist, 2019, v. 224 n. 4, p. 1557-1568 | - |
dc.identifier.issn | 0028-646X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276030 | - |
dc.description.abstract | Leaf‐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.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at https://nph-onlinelibrary-wiley-com.eproxy.lib.hku.hk/journal/14698137 | - |
dc.relation.ispartof | New Phytologist | - |
dc.rights | This 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.subject | leaf mass area | - |
dc.subject | partial least-squares regression (PLSR) | - |
dc.subject | plant traits | - |
dc.subject | remote sensing | - |
dc.subject | specific leaf area | - |
dc.subject | spectroscopy | - |
dc.title | From the Arctic to the tropics: multi‐biome prediction of leaf mass per area using leaf reflectance | - |
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/nph.16123 | - |
dc.identifier.scopus | eid_2-s2.0-85073966982 | - |
dc.identifier.hkuros | 302692 | - |
dc.identifier.volume | 224 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1557 | - |
dc.identifier.epage | 1568 | - |
dc.identifier.isi | WOS:000486883100001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0028-646X | - |