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MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess

2016-07Journal article Open Access
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dc.contributor.authorCristina, SĂłnia
dc.contributor.authorCordeiro, Clara
dc.contributor.authorLavender, Samantha
dc.contributor.authorGoela, Priscila
dc.contributor.authorIcely, John
dc.contributor.authorNewton, Alice
dc.date.accessioned2017-04-07T15:56:32Z
dc.date.available2017-04-07T15:56:32Z
dc.date.issued2016-07
dc.description.abstractThe European Space Agency has acquired 10 years of data on the temporal and spatial distribution of phytoplankton biomass from the MEdium Resolution Imaging Spectrometer (MERIS) sensor for ocean color. The phytoplankton biomass was estimated with the MERIS product Algal Pigment Index 1 (API 1). Seasonal-Trend decomposition of time series based on Loess (STL) identified the temporal variability of the dynamical features in the MERIS products for water leaving reflectance ((w)()) and API 1. The advantages of STL is that it can identify seasonal components changing over time, it is responsive to nonlinear trends, and it is robust in the presence of outliers. One of the novelties in this study is the development and the implementation of an automatic procedure, stl.fit(), that searches the best data modeling by varying the values of the smoothing parameters, and by selecting the model with the lowest error measure. This procedure was applied to 10 years of monthly time series from Sagres in the Southwestern Iberian Peninsula at three Stations, 2, 10 and 18 km from the shore. Decomposing the MERIS products into seasonal, trend and irregular components with stl.fit(), the (w)() indicated dominance of the seasonal and irregular components while API 1 was mainly dominated by the seasonal component, with an increasing effect from inshore to offshore. A comparison of the seasonal components between the (w)() and the API 1 product, showed that the variations decrease along this time period due to the changes in phytoplankton functional types. Furthermore, inter-annual seasonal variation for API 1 showed the influence of upwelling events and in which month of the year these occur at each of the three Sagres stations. The stl.fit() is a good tool for any remote sensing study of time series, particularly those addressing inter-annual variations. This procedure will be made available in R software.
dc.identifier.doi10.3390/rs8060449
dc.identifier.issn2072-4292
dc.identifier.otherAUT: ANE00265;
dc.identifier.urihttp://hdl.handle.net/10400.1/9449
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationASSINATURA ESPECTRAL DAS COMUNIDADES FITOPLANCTÓNICAS NA COSTA SUDOESTE DE PORTUGAL: IMPACTO NOS DADOS DE DETECÇÃO REMOTA
dc.relationAQUA-USERS: AQUAculture USEr driven operational Remote Sensing information services
dc.relationEcosystem Approach to making Space for Aquaculture
dc.relationDEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status
dc.relation.isbasedonWOS:000379985300009
dc.subjectMERIS
dc.subjectTime series
dc.subjectSeasonal-trend decomposition
dc.subjectAlgal pigment index 1
dc.subjectWater leaving reflectance
dc.subjectInter-annual seasonal variability
dc.subjectIberian Peninsula
dc.subjectSagres
dc.titleMERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleASSINATURA ESPECTRAL DAS COMUNIDADES FITOPLANCTÓNICAS NA COSTA SUDOESTE DE PORTUGAL: IMPACTO NOS DADOS DE DETECÇÃO REMOTA
oaire.awardTitleAQUA-USERS: AQUAculture USEr driven operational Remote Sensing information services
oaire.awardTitleEcosystem Approach to making Space for Aquaculture
oaire.awardTitleDEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F78354%2F2011/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F78356%2F2011/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/607325/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/633476/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMAT%2F00006%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/308392/EU
oaire.citation.endPage449
oaire.citation.issue6
oaire.citation.startPage449
oaire.citation.titleRemote Sensing
oaire.citation.volume8
oaire.fundingStreamSFRH
oaire.fundingStreamFP7
oaire.fundingStreamH2020
oaire.fundingStream5876
oaire.fundingStreamFP7
person.familyNameCristina
person.familyNameGoela
person.familyNameIcely
person.familyNameNewton
person.givenNameSĂłnia
person.givenNamePriscila
person.givenNameJohn
person.givenNameAlice
person.identifier333937
person.identifier.ciencia-idB713-3911-1803
person.identifier.ciencia-id6F13-1247-B2B7
person.identifier.orcid0000-0002-5716-9750
person.identifier.orcid0000-0001-9786-1269
person.identifier.orcid0000-0002-9114-8283
person.identifier.orcid0000-0001-9286-5914
person.identifier.scopus-author-id55957312000
person.identifier.scopus-author-id6506626316
person.identifier.scopus-author-id7201391894
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameFundação para a CiĂȘncia e a Tecnologia
project.funder.nameFundação para a CiĂȘncia e a Tecnologia
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
project.funder.nameFundação para a CiĂȘncia e a Tecnologia
project.funder.nameEuropean Commission
rcaap.rightsopenAccess
rcaap.typearticle
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