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dc.contributor.authorPacheco-Labrador, Javier-
dc.contributor.authorEl-Madany, Tarek S.-
dc.contributor.authorMartín, M. Pilar-
dc.contributor.authorMigliavacca, Mirco-
dc.contributor.authorRossini, Micol-
dc.contributor.authorCarrara, Arnaud-
dc.contributor.authorZarco-Tejada, Pablo J.-
dc.date.accessioned2020-04-16T10:45:19Z-
dc.date.available2020-04-16T10:45:19Z-
dc.date.issued2017-
dc.identifierdoi: http://dx.doi.org/10.3390/rs9060608-
dc.identifiere-issn: 2072-4292-
dc.identifier.citationRemote Sensing 9(608) 1-25(2017)-
dc.identifier.urihttp://hdl.handle.net/10261/207863-
dc.description.abstractSpatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPP) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems.-
dc.description.sponsorshipThis research was funded by the following projects: FLUχPEC “Monitoring changes in water and carbon fluxes from remote and proximal sensing in a Mediterranean dehesa ecosystem” (http://www.lineas.cchs.csic.es/fluxpec) (CGL2012-34383, Spanish Ministry of Economy and Competitiveness), DEHESHyrE Transnational Access Project (EUFAR), MaNiP “MAnipulation NItrogen and Phosporous” (https://www.bgc-jena.mpg.de/bgi/index.php/Research/ManipProject) (MPI-BGC and the Alexander Von Humsboldt Foundation through the Markus Reichstein Prize), BIOSPEC “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (http://www.lineas.cchs.csic.es/biospec) (CGL2008-02301/CLI, Ministry of Science and Innovation) and CEOS-Spain (http://ceosspain.lpi.uv.es/home/project) (AYA2011-29334-C02-01, Ministry of Economy and Competitiveness); FLUχPEC: “Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem” (http://www.lineas.cchs.csic.es/ fluxpec) (CGL2012-34383, Spanish Ministry of Economy and Competitiveness); and the EnMAP project MoReDEHESHyReS “Modelling Responses of Dehesas with Hyperspectral Remote Sensing” (Contract No. 50EE1621) (https://www.bgc-jena.mpg.de/bgi/index.php/Research/MoReDEHESHyReS) (German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy-
dc.languageeng-
dc.publisherMultidisciplinary Digital Publishing Institute-
dc.relationCGL2012-34383-
dc.relationCGL2008-02301/CL-
dc.relationAYA2011-29334-C02-01-
dc.relation.isversionofPublisher's version-
dc.rightsopenAccess-
dc.subjectGross Primary Production (GPP)-
dc.subjectRemote Sensing (RS)-
dc.subjectFootprint-
dc.subjectMismatch-
dc.subjectLight use efficiency-
dc.subjectFPAR-
dc.subjectPRI-
dc.subjectSemi-empirical GPP model-
dc.subjectMODIS GPP-
dc.titleSpatio-temporal relationships between optical information and carbon fluxes in a mediterranean tree-grass ecosystem-
dc.typeartículo-
dc.identifier.doihttp://dx.doi.org/10.3390/rs9060608-
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/9/6/608-
dc.date.updated2020-04-16T10:45:20Z-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.contributor.funderMinisterio de Ciencia e Innovación (España)-
dc.contributor.funderFederal Ministry of Economics and Technology (Germany)-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100004837es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100002765es_ES
dc.contributor.orcidPacheco-Labrador, Javier [0000-0003-3401-7081]-
dc.contributor.orcidCarrara, Arnaud [0000-0002-9095-8807]-
dc.contributor.orcidMartín, M. Pilar [0000-0002-5563-8461]-
dc.contributor.orcidZarco-Tejada, Pablo J. [0000-0003-1433-6165]-
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