Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
Identifieur interne : 001510 ( Main/Exploration ); précédent : 001509; suivant : 001511Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
Auteurs : Mallory L. Barnes [États-Unis] ; David D. Breshears [États-Unis] ; Darin J. Law [États-Unis] ; Willem J D. Van Leeuwen [États-Unis] ; Russell K. Monson [États-Unis] ; Alec C. Fojtik [États-Unis] ; Greg A. Barron-Gafford [États-Unis] ; David J P. Moore [États-Unis]Source :
- PloS one [ 1932-6203 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- métabolisme : Chlorophylle, Feuilles de plante.
- physiologie : Feuilles de plante.
- Analyse de régression, Photosynthèse, Saisons.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Chlorophyll.
- metabolism : Plant Leaves.
- physiology : Plant Leaves.
- Photosynthesis, Regression Analysis, Seasons.
Abstract
Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.
DOI: 10.1371/journal.pone.0189539
PubMed: 29281709
PubMed Central: PMC5744967
Affiliations:
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Van Leeuwen</name><affiliation wicri:level="2"><nlm:affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>School of Geography and Development, University of Arizona, Tucson, Arizona, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Geography and Development, University of Arizona, Tucson, Arizona</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation></author><author><name sortKey="Monson, Russell K" sort="Monson, Russell K" uniqKey="Monson R" first="Russell K" last="Monson">Russell K. 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Barron-Gafford</name><affiliation wicri:level="2"><nlm:affiliation>School of Geography and Development, University of Arizona, Tucson, Arizona, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Geography and Development, University of Arizona, Tucson, Arizona</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation></author><author><name sortKey="Moore, David J P" sort="Moore, David J P" uniqKey="Moore D" first="David J P" last="Moore">David J P. Moore</name><affiliation wicri:level="2"><nlm:affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Chlorophyll (metabolism)</term><term>Photosynthesis (MeSH)</term><term>Plant Leaves (metabolism)</term><term>Plant Leaves (physiology)</term><term>Regression Analysis (MeSH)</term><term>Seasons (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de régression (MeSH)</term><term>Chlorophylle (métabolisme)</term><term>Feuilles de plante (métabolisme)</term><term>Feuilles de plante (physiologie)</term><term>Photosynthèse (MeSH)</term><term>Saisons (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Chlorophyll</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Plant Leaves</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Chlorophylle</term><term>Feuilles de plante</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Feuilles de plante</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Plant Leaves</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Photosynthesis</term><term>Regression Analysis</term><term>Seasons</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de régression</term><term>Photosynthèse</term><term>Saisons</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29281709</PMID><DateCompleted><Year>2018</Year><Month>01</Month><Day>29</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>12</Issue><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.</ArticleTitle><Pagination><MedlinePgn>e0189539</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0189539</ELocationID><Abstract><AbstractText>Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Barnes</LastName><ForeName>Mallory L</ForeName><Initials>ML</Initials><Identifier Source="ORCID">0000-0001-8528-6981</Identifier><AffiliationInfo><Affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Breshears</LastName><ForeName>David D</ForeName><Initials>DD</Initials><AffiliationInfo><Affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Law</LastName><ForeName>Darin J</ForeName><Initials>DJ</Initials><AffiliationInfo><Affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>van Leeuwen</LastName><ForeName>Willem J D</ForeName><Initials>WJD</Initials><AffiliationInfo><Affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Geography and Development, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Monson</LastName><ForeName>Russell K</ForeName><Initials>RK</Initials><AffiliationInfo><Affiliation>Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fojtik</LastName><ForeName>Alec C</ForeName><Initials>AC</Initials><AffiliationInfo><Affiliation>Department of Geology, Wheaton College, Wheaton, Illinois, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Barron-Gafford</LastName><ForeName>Greg A</ForeName><Initials>GA</Initials><AffiliationInfo><Affiliation>School of Geography and Development, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Moore</LastName><ForeName>David J P</ForeName><Initials>DJP</Initials><AffiliationInfo><Affiliation>School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>12</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>1406-65-1</RegistryNumber><NameOfSubstance UI="D002734">Chlorophyll</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002734" MajorTopicYN="N">Chlorophyll</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName 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IdType="pubmed">11276416</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Arizona</li><li>Illinois</li></region></list><tree><country name="États-Unis"><region name="Arizona"><name sortKey="Barnes, Mallory L" sort="Barnes, Mallory L" uniqKey="Barnes M" first="Mallory L" last="Barnes">Mallory L. Barnes</name></region><name sortKey="Barron Gafford, Greg A" sort="Barron Gafford, Greg A" uniqKey="Barron Gafford G" first="Greg A" last="Barron-Gafford">Greg A. Barron-Gafford</name><name sortKey="Breshears, David D" sort="Breshears, David D" uniqKey="Breshears D" first="David D" last="Breshears">David D. Breshears</name><name sortKey="Breshears, David D" sort="Breshears, David D" uniqKey="Breshears D" first="David D" last="Breshears">David D. Breshears</name><name sortKey="Fojtik, Alec C" sort="Fojtik, Alec C" uniqKey="Fojtik A" first="Alec C" last="Fojtik">Alec C. Fojtik</name><name sortKey="Law, Darin J" sort="Law, Darin J" uniqKey="Law D" first="Darin J" last="Law">Darin J. Law</name><name sortKey="Monson, Russell K" sort="Monson, Russell K" uniqKey="Monson R" first="Russell K" last="Monson">Russell K. Monson</name><name sortKey="Moore, David J P" sort="Moore, David J P" uniqKey="Moore D" first="David J P" last="Moore">David J P. Moore</name><name sortKey="Van Leeuwen, Willem J D" sort="Van Leeuwen, Willem J D" uniqKey="Van Leeuwen W" first="Willem J D" last="Van Leeuwen">Willem J D. Van Leeuwen</name><name sortKey="Van Leeuwen, Willem J D" sort="Van Leeuwen, Willem J D" uniqKey="Van Leeuwen W" first="Willem J D" last="Van Leeuwen">Willem J D. Van Leeuwen</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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