Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.
Identifieur interne : 003563 ( Main/Exploration ); précédent : 003562; suivant : 003564Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.
Auteurs : Mirco Migliavacca [Italie] ; Michele Meroni ; Lorenzo Busetto ; Roberto Colombo ; Terenzio Zenone ; Giorgio Matteucci ; Giovanni Manca ; Guenther SeufertSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2009.
Abstract
In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.
DOI: 10.3390/s90200922
PubMed: 22399948
PubMed Central: PMC3280840
Affiliations:
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We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">22399948</PMID><DateCompleted><Year>2012</Year><Month>09</Month><Day>10</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1424-8220</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><Issue>2</Issue><PubDate><Year>2009</Year></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.</ArticleTitle><Pagination><MedlinePgn>922-42</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s90200922</ELocationID><Abstract><AbstractText>In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Migliavacca</LastName><ForeName>Mirco</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Milano, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Meroni</LastName><ForeName>Michele</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Busetto</LastName><ForeName>Lorenzo</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Colombo</LastName><ForeName>Roberto</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Zenone</LastName><ForeName>Terenzio</ForeName><Initials>T</Initials></Author><Author 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IdType="pii">s90200922</ArticleId><ArticleId IdType="pmc">PMC3280840</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Int J Biometeorol. 2003 Aug;47(4):193-201</Citation><ArticleIdList><ArticleId IdType="pubmed">12698325</ArticleId></ArticleIdList></Reference><Reference><Citation>Tree Physiol. 2005 Jul;25(7):839-57</Citation><ArticleIdList><ArticleId IdType="pubmed">15870053</ArticleId></ArticleIdList></Reference><Reference><Citation>Environ Pollut. 2005 Oct;137(3):525-35</Citation><ArticleIdList><ArticleId IdType="pubmed">16005764</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Biometeorol. 2008 Sep;52(7):587-605</Citation><ArticleIdList><ArticleId IdType="pubmed">18437430</ArticleId></ArticleIdList></Reference><Reference><Citation>Ecol Appl. 1991 May;1(2):157-167</Citation><ArticleIdList><ArticleId IdType="pubmed">27755662</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Italie</li></country></list><tree><noCountry><name sortKey="Busetto, Lorenzo" sort="Busetto, Lorenzo" uniqKey="Busetto L" first="Lorenzo" last="Busetto">Lorenzo Busetto</name><name sortKey="Colombo, Roberto" sort="Colombo, Roberto" uniqKey="Colombo R" first="Roberto" last="Colombo">Roberto Colombo</name><name sortKey="Manca, Giovanni" sort="Manca, Giovanni" uniqKey="Manca G" first="Giovanni" last="Manca">Giovanni Manca</name><name sortKey="Matteucci, Giorgio" sort="Matteucci, Giorgio" uniqKey="Matteucci G" first="Giorgio" last="Matteucci">Giorgio Matteucci</name><name sortKey="Meroni, Michele" sort="Meroni, Michele" uniqKey="Meroni M" first="Michele" last="Meroni">Michele Meroni</name><name sortKey="Seufert, Guenther" sort="Seufert, Guenther" uniqKey="Seufert G" first="Guenther" last="Seufert">Guenther Seufert</name><name sortKey="Zenone, Terenzio" sort="Zenone, Terenzio" uniqKey="Zenone T" first="Terenzio" last="Zenone">Terenzio Zenone</name></noCountry><country name="Italie"><noRegion><name sortKey="Migliavacca, Mirco" sort="Migliavacca, Mirco" uniqKey="Migliavacca M" first="Mirco" last="Migliavacca">Mirco Migliavacca</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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