Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/rs11192283 http://hdl.handle.net/11449/196331 |
Resumo: | Suspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tiete River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m(3), turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since K-d is not susceptible to the saturation issue encountered when using remote sensing reflectance (R-rs), we estimate SPM concentrations via K-d. K-d was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using K-d(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%. |
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Repositório Institucional da UNESP |
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Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Schemesemi-analytical modelinherent optical propertieslight attenuationwater quality monitoringSuspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tiete River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m(3), turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since K-d is not susceptible to the saturation issue encountered when using remote sensing reflectance (R-rs), we estimate SPM concentrations via K-d. K-d was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using K-d(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Sao Paulo State Univ Unesp, Dept Cartog, BR-19063841 Presidente Prudente, SP, BrazilNanyang Technol Univ, Natl Inst Educ, Singapore 639798, SingaporeSao Paulo State Univ Unesp, Dept Environm Engn, BR-12247004 Sao Jose Dos Campos, SP, BrazilSao Paulo State Univ Unesp, Dept Cartog, BR-19063841 Presidente Prudente, SP, BrazilSao Paulo State Univ Unesp, Dept Environm Engn, BR-12247004 Sao Jose Dos Campos, SP, BrazilFAPESP: 2012/19821-1FAPESP: 2015/21586-9FAPESP: 2015/18525-8FAPESP: 2019/00259-0CNPq: 472131/2012-5CNPq: 482605/2013-8MdpiUniversidade Estadual Paulista (Unesp)Nanyang Technol UnivBernardo, Nariane [UNESP]Carmo, Alisson do [UNESP]Park, EdwardAlcantara, Enner [UNESP]2020-12-10T19:41:12Z2020-12-10T19:41:12Z2019-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article22http://dx.doi.org/10.3390/rs11192283Remote Sensing. Basel: Mdpi, v. 11, n. 19, 22 p., 2019.http://hdl.handle.net/11449/19633110.3390/rs11192283WOS:000496827100097Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:01:39Zoai:repositorio.unesp.br:11449/196331Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:14:14.273721Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
title |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
spellingShingle |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme Bernardo, Nariane [UNESP] semi-analytical model inherent optical properties light attenuation water quality monitoring |
title_short |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
title_full |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
title_fullStr |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
title_full_unstemmed |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
title_sort |
Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme |
author |
Bernardo, Nariane [UNESP] |
author_facet |
Bernardo, Nariane [UNESP] Carmo, Alisson do [UNESP] Park, Edward Alcantara, Enner [UNESP] |
author_role |
author |
author2 |
Carmo, Alisson do [UNESP] Park, Edward Alcantara, Enner [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Nanyang Technol Univ |
dc.contributor.author.fl_str_mv |
Bernardo, Nariane [UNESP] Carmo, Alisson do [UNESP] Park, Edward Alcantara, Enner [UNESP] |
dc.subject.por.fl_str_mv |
semi-analytical model inherent optical properties light attenuation water quality monitoring |
topic |
semi-analytical model inherent optical properties light attenuation water quality monitoring |
description |
Suspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tiete River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m(3), turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since K-d is not susceptible to the saturation issue encountered when using remote sensing reflectance (R-rs), we estimate SPM concentrations via K-d. K-d was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using K-d(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-01 2020-12-10T19:41:12Z 2020-12-10T19:41:12Z |
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://dx.doi.org/10.3390/rs11192283 Remote Sensing. Basel: Mdpi, v. 11, n. 19, 22 p., 2019. http://hdl.handle.net/11449/196331 10.3390/rs11192283 WOS:000496827100097 |
url |
http://dx.doi.org/10.3390/rs11192283 http://hdl.handle.net/11449/196331 |
identifier_str_mv |
Remote Sensing. Basel: Mdpi, v. 11, n. 19, 22 p., 2019. 10.3390/rs11192283 WOS:000496827100097 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
22 |
dc.publisher.none.fl_str_mv |
Mdpi |
publisher.none.fl_str_mv |
Mdpi |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129039331753984 |