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

Detalhes bibliográficos
Autor(a) principal: Cristina, Sónia
Data de Publicação: 2016
Outros Autores: Cordeiro, Clara, Lavender, Samantha, Goela, Priscila, Icely, John, Newton, Alice
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/9449
Resumo: The 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.
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spelling MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loessMERISTime seriesSeasonal-trend decompositionAlgal pigment index 1Water leaving reflectanceInter-annual seasonal variabilityIberian PeninsulaSagresThe 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.MDPISapientiaCristina, SóniaCordeiro, ClaraLavender, SamanthaGoela, PriscilaIcely, JohnNewton, Alice2017-04-07T15:56:32Z2016-072016-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/9449eng2072-4292AUT: ANE00265;10.3390/rs8060449info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:20:54Zoai:sapientia.ualg.pt:10400.1/9449Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:01:23.704650Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
title MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
spellingShingle MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
Cristina, Sónia
MERIS
Time series
Seasonal-trend decomposition
Algal pigment index 1
Water leaving reflectance
Inter-annual seasonal variability
Iberian Peninsula
Sagres
title_short MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
title_full MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
title_fullStr MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
title_full_unstemmed MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
title_sort MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loess
author Cristina, Sónia
author_facet Cristina, Sónia
Cordeiro, Clara
Lavender, Samantha
Goela, Priscila
Icely, John
Newton, Alice
author_role author
author2 Cordeiro, Clara
Lavender, Samantha
Goela, Priscila
Icely, John
Newton, Alice
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Cristina, Sónia
Cordeiro, Clara
Lavender, Samantha
Goela, Priscila
Icely, John
Newton, Alice
dc.subject.por.fl_str_mv MERIS
Time series
Seasonal-trend decomposition
Algal pigment index 1
Water leaving reflectance
Inter-annual seasonal variability
Iberian Peninsula
Sagres
topic MERIS
Time series
Seasonal-trend decomposition
Algal pigment index 1
Water leaving reflectance
Inter-annual seasonal variability
Iberian Peninsula
Sagres
description The 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.
publishDate 2016
dc.date.none.fl_str_mv 2016-07
2016-07-01T00:00:00Z
2017-04-07T15:56:32Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/9449
url http://hdl.handle.net/10400.1/9449
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
AUT: ANE00265;
10.3390/rs8060449
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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