Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil

Detalhes bibliográficos
Autor(a) principal: Jucá,Marcella Vasconcelos Quintella
Data de Publicação: 2022
Outros Autores: Ribeiro Neto,Alfredo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: RBRH (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100213
Resumo: ABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.
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spelling Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast BrazilSMOS satelliteSemiaridNortheast BrazilLand surface modelsABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.Associação Brasileira de Recursos Hídricos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100213RBRH v.27 2022reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.272220220016info:eu-repo/semantics/openAccessJucá,Marcella Vasconcelos QuintellaRibeiro Neto,Alfredoeng2022-07-15T00:00:00Zoai:scielo:S2318-03312022000100213Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2022-07-15T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false
dc.title.none.fl_str_mv Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
title Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
spellingShingle Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
Jucá,Marcella Vasconcelos Quintella
SMOS satellite
Semiarid
Northeast Brazil
Land surface models
title_short Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
title_full Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
title_fullStr Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
title_full_unstemmed Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
title_sort Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil
author Jucá,Marcella Vasconcelos Quintella
author_facet Jucá,Marcella Vasconcelos Quintella
Ribeiro Neto,Alfredo
author_role author
author2 Ribeiro Neto,Alfredo
author2_role author
dc.contributor.author.fl_str_mv Jucá,Marcella Vasconcelos Quintella
Ribeiro Neto,Alfredo
dc.subject.por.fl_str_mv SMOS satellite
Semiarid
Northeast Brazil
Land surface models
topic SMOS satellite
Semiarid
Northeast Brazil
Land surface models
description ABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100213
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100213
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2318-0331.272220220016
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
dc.source.none.fl_str_mv RBRH v.27 2022
reponame:RBRH (Online)
instname:Associação Brasileira de Recursos Hídricos (ABRH)
instacron:ABRH
instname_str Associação Brasileira de Recursos Hídricos (ABRH)
instacron_str ABRH
institution ABRH
reponame_str RBRH (Online)
collection RBRH (Online)
repository.name.fl_str_mv RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)
repository.mail.fl_str_mv ||rbrh@abrh.org.br
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