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hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
hal.structure.identifierDepartment of Applied Ecology and Environmental Biology
hal.structure.identifierDepartment of Organismic and Evolutionary Biology
dc.contributor.authorHUFKENS, Koen
hal.structure.identifierDepartment of Organismic and Evolutionary Biology
dc.contributor.authorBASLER, David
hal.structure.identifierUniversity of New Hampshire [UNH]
dc.contributor.authorMILLIMAN, Tom
hal.structure.identifierBoston University [Boston] [BU]
dc.contributor.authorMELAAS, Eli K.
hal.structure.identifierDepartment of Organismic and Evolutionary Biology
hal.structure.identifierSchool of Informatics, Computing, and Cyber Systems [SICCS]
hal.structure.identifierNorthern Arizona University [Flagstaff]
dc.contributor.authorRICHARDSON, Andrew D.
dc.date.accessioned2024-04-08T12:05:41Z
dc.date.available2024-04-08T12:05:41Z
dc.date.issued2018
dc.identifier.issn2041-210X
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196369
dc.description.abstractEn<strong>1.</strong> Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. <strong>2.</strong> Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. <strong>3.</strong> We show an example analysis, using the PHENOR modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. <strong>4.</strong> In conclusion, we hope the PHENOR phenology modelling framework in the R language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.
dc.language.isoen
dc.publisherWiley
dc.subjectphenology
dc.subjectUSA-NPN
dc.subject.enmodelling
dc.subject.enMODIS land surface phenology
dc.subject.enPEP725
dc.subject.enPHENOCAM
dc.subject.enR package
dc.title.enAn integrated phenology modelling framework in R
dc.typeArticle de revue
dc.identifier.doi10.1111/2041-210X.12970
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences de l'environnement
bordeaux.journalMethods in Ecology and Evolution
bordeaux.page1276-1285
bordeaux.volume9
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.issue5
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-02622109
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02622109v1
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.jtitle=Methods%20in%20Ecology%20and%20Evolution&amp;rft.date=2018&amp;rft.volume=9&amp;rft.issue=5&amp;rft.spage=1276-1285&amp;rft.epage=1276-1285&amp;rft.eissn=2041-210X&amp;rft.issn=2041-210X&amp;rft.au=HUFKENS,%20Koen&amp;BASLER,%20David&amp;MILLIMAN,%20Tom&amp;MELAAS,%20Eli%20K.&amp;RICHARDSON,%20Andrew%20D.&amp;rft.genre=article


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