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Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal

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Resumo:The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.
Autores principais:Figueiredo, Tomás de
Outros Autores:Royer, Ana Caroline; Fonseca, Felícia; Schütz, Fabiana Costa Araújo; Hernández, Zulimar
Assunto:Remote sensing Radar satellite data Active and passive microwave sensors ESA CCI SM product Soil water balance Soil water storage Regression models Hysteresis
Ano:2021
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
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author Figueiredo, Tomás de
author2 Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
author2_role author
author
author
author
author_facet Figueiredo, Tomás de
Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_txt [{\"Person.name\":\"Figueiredo, Tomás de\",\"Person.identifier.orcid\":\"0000-0001-7690-8996\"},{\"Person.name\":\"Royer, Ana Caroline\",\"Person.identifier.orcid\":\"0000-0002-0746-185X\"},{\"Person.name\":\"Fonseca, Felícia\",\"Person.identifier.orcid\":\"0000-0001-7727-071X\"},{\"Person.name\":\"Schütz, Fabiana Costa Araújo\"},{\"Person.name\":\"Hernández, Zulimar\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Figueiredo, Tomás de
Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
datacite.date.Accepted.fl_str_mv 2021-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2021-09-16T10:28:51Z
datacite.date.embargoed.fl_str_mv 2021-09-16T10:28:51Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
datacite.titles.title.fl_str_mv Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Figueiredo, Tomás de
Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
dc.date.Accepted.fl_str_mv 2021-01-01T00:00:00Z
dc.date.available.fl_str_mv 2021-09-16T10:28:51Z
dc.date.embargoed.fl_str_mv 2021-09-16T10:28:51Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/23915
dc.language.none.fl_str_mv eng
dc.rights.cclincense.fl_str_mv http://creativecommons.org/licenses/by/4.0/
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
dc.title.fl_str_mv Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_6501
description The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.
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eu_rights_str_mv openAccess
format article
fulltext.url.fl_str_mv https://bibliotecadigital.ipb.pt/bitstreams/a05e04a9-e27f-4ead-ab63-9b04ecbb9e5e/download
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identifier.url.fl_str_mv http://hdl.handle.net/10198/23915
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institution Instituto Politécnico de Bragança
instname_str Instituto Politécnico de Bragança
language eng
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network_name_str Biblioteca Digital do IPB
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/23915
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Figueiredo, Tomás de
Figueiredo, Tomás de
https://www.ciencia-id.pt/961D-607D-51CC
961D-607D-51CC
http://orcid.org/0000-0001-7690-8996
0000-0001-7690-8996
Royer, Ana Caroline
Royer, Ana Caroline
https://www.ciencia-id.pt/721A-9374-4C35
721A-9374-4C35
http://orcid.org/0000-0002-0746-185X
0000-0002-0746-185X
Fonseca, Felícia
Fonseca, Felícia
http://orcid.org/0000-0001-7727-071X
0000-0001-7727-071X
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
publishDate 2021
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling engpt_PTThe European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.application/pdfpt_PTRegression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast PortugalPersonalFigueiredo, Tomás deDSpacehttp://dspace.org/items/db897e48-ecf7-4ce1-ba27-369260337510DSpacehttp://dspace.org/items/db897e48-ecf7-4ce1-ba27-369260337510FigueiredoTomás d'AquinoCiência IDhttps://www.ciencia-id.pt961D-607D-51CCORCIDhttp://orcid.org0000-0001-7690-8996Scopus Author IDhttps://www.scopus.com54790554500PersonalRoyer, Ana CarolineDSpacehttp://dspace.org/items/c0fb97bf-0c68-4bf9-8994-ce6dcdb6d86fDSpacehttp://dspace.org/items/c0fb97bf-0c68-4bf9-8994-ce6dcdb6d86fRoyerAna CarolineCiência IDhttps://www.ciencia-id.pt721A-9374-4C35ORCIDhttp://orcid.org0000-0002-0746-185XPersonalFonseca, FelíciaDSpacehttp://dspace.org/items/4f6f8be1-73c1-45bb-b159-ce3f8ff96c84DSpacehttp://dspace.org/items/4f6f8be1-73c1-45bb-b159-ce3f8ff96c84FonsecaFelíciaORCIDhttp://orcid.org0000-0001-7727-071XScopus Author IDhttps://www.scopus.com36970960500Schütz, Fabiana Costa AraújoHernández, ZulimarHostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISSNIsPartOf2073-4441DOIIsPartOf10.3390/w130100372021-09-16T10:28:51Z20212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/23915http://purl.org/coar/access_right/c_abf2open accessRemote sensingRadar satellite dataActive and passive microwave sensorsESA CCI SM productSoil water balanceSoil water storageRegression modelsHysteresis3126638 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2021http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/a05e04a9-e27f-4ead-ab63-9b04ecbb9e5e/downloadWater13137
spellingShingle Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
Figueiredo, Tomás de
Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
status SINGLETON
subject.fl_str_mv Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
title Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title_full Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title_fullStr Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title_full_unstemmed Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title_short Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title_sort Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
topic Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
topic_facet Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
url http://hdl.handle.net/10198/23915
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