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

Detalhes bibliográficos
Autor(a) principal: Figueiredo, Tomás de
Data de Publicação: 2021
Outros Autores: Royer, Ana Caroline, Fonseca, Felícia, Schütz, Fabiana Costa Araújo, Hernández, Zulimar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10198/23915
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.
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spelling Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast PortugalRemote sensingRadar satellite dataActive and passive microwave sensorsESA CCI SM productSoil water balanceSoil water storageRegression modelsHysteresisThe 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.This research was partially funded by the Foundation for Science and Technology (FCT, Lisbon, Portugal), grant number UIDB/00690/2020. Furthermore, A.C.R.’s contribution to the research was financially supported, first, by the Instituto Politécnico de Bragança through the Double Diploma MSc programme in Environmental Technology with the Technological Federal University of Paraná, Brazil, and second, by the EU FEDER Fund, through the POCTEP programme funding the TERRAMATER research project (grant 0701_TERRAMATER_1_E).Biblioteca Digital do IPBFigueiredo, Tomás deRoyer, Ana CarolineFonseca, FelíciaSchütz, Fabiana Costa AraújoHernández, Zulimar2021-09-16T10:28:51Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/23915engFigueiredo, Tomás de; Royer, Ana Carolina; Fonseca, Felícia; Schütz, Fabiana Costa de Araújo; Hernandez Hernandez, Zulimar (2021). Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal. Water. ISSN 2073-4441. 13:1, p. 1-262073-444110.3390/w13010037info: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:RCAAP2023-11-21T10:53:29Zoai:bibliotecadigital.ipb.pt:10198/23915Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:14:49.963580Repositório 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 Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
title Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
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
title_short 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_sort Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal
author Figueiredo, Tomás de
author_facet Figueiredo, Tomás de
Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
author_role author
author2 Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Figueiredo, Tomás de
Royer, Ana Caroline
Fonseca, Felícia
Schütz, Fabiana Costa Araújo
Hernández, Zulimar
dc.subject.por.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
topic Remote sensing
Radar satellite data
Active and passive microwave sensors
ESA CCI SM product
Soil water balance
Soil water storage
Regression models
Hysteresis
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.
publishDate 2021
dc.date.none.fl_str_mv 2021-09-16T10:28:51Z
2021
2021-01-01T00:00:00Z
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/10198/23915
url http://hdl.handle.net/10198/23915
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Figueiredo, Tomás de; Royer, Ana Carolina; Fonseca, Felícia; Schütz, Fabiana Costa de Araújo; Hernandez Hernandez, Zulimar (2021). Regression models for soil water storage estimation using the ESA CCI satellite soil moisture product: a case study in Northeast Portugal. Water. ISSN 2073-4441. 13:1, p. 1-26
2073-4441
10.3390/w13010037
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.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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