PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES

Detalhes bibliográficos
Autor(a) principal: Alcantara, Enner [UNESP]
Data de Publicação: 2018
Outros Autores: Andrade, Caroline Pilfer de [UNESP], Gomes, Ana Carolina [UNESP], Bernardo, Nariane [UNESP], Carmo, Alisson Fernando [UNESP], Rodrigues, Thanan, Watanabe, Fernanda [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/185095
Resumo: Remote sensing can be a powerful tool for long-teen spatial and temporal water quality monitoring if proper sets of algorithms are available. To estimate optically significant substances (OSS) by satellite images the water-leaving reflectance (pw) must be accurately estimated because it is directly related to the inherent optical properties (IOPs). For an accurate pw an effective atmospheric correction method must be used to remote the contribution of the atmospheric path radiance. The C2RCC processor has a set of algorithms capable of reduce the atmospheric path radiance, estimate the IOPs and then the OSS concentrations. But, the C2RCC was only tested using OL Cl/Sentine1-3 images for coastal areas, therefore, is of huge importance to know about their accuracy for inland waters. The results showed that the pw (with errors from 26.57 to 97.48%), IOPs (with errors from 39.77 to 99.90%) and OSS concentrations (with errors from 49.29 to 148.40%) estimated by C2RCC have no correlation with in situ data. For a long-teen use of OL Cl/Sentine1-3 images researchers must try to use another atmospheric correction and IOPs estimation methods when studying inland waters.
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spelling PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGESSentinel-3C2RCCinland waterIOPsRemote sensing can be a powerful tool for long-teen spatial and temporal water quality monitoring if proper sets of algorithms are available. To estimate optically significant substances (OSS) by satellite images the water-leaving reflectance (pw) must be accurately estimated because it is directly related to the inherent optical properties (IOPs). For an accurate pw an effective atmospheric correction method must be used to remote the contribution of the atmospheric path radiance. The C2RCC processor has a set of algorithms capable of reduce the atmospheric path radiance, estimate the IOPs and then the OSS concentrations. But, the C2RCC was only tested using OL Cl/Sentine1-3 images for coastal areas, therefore, is of huge importance to know about their accuracy for inland waters. The results showed that the pw (with errors from 26.57 to 97.48%), IOPs (with errors from 39.77 to 99.90%) and OSS concentrations (with errors from 49.29 to 148.40%) estimated by C2RCC have no correlation with in situ data. For a long-teen use of OL Cl/Sentine1-3 images researchers must try to use another atmospheric correction and IOPs estimation methods when studying inland waters.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Sao Paulo State Univ, UNESP, Dept Environm Engn, Sao Jose Dos Campos, SP, BrazilSao Paulo State Univ, UNESP, Dept Cartog, Presidente Prudente, SP, BrazilFed Inst Educ Sci & Technol Para, Castanhal, PA, BrazilSao Paulo State Univ, UNESP, Dept Environm Engn, Sao Jose Dos Campos, SP, BrazilSao Paulo State Univ, UNESP, Dept Cartog, Presidente Prudente, SP, BrazilFAPESP: 2015/21586-9IeeeUniversidade Estadual Paulista (Unesp)Fed Inst Educ Sci & Technol ParaAlcantara, Enner [UNESP]Andrade, Caroline Pilfer de [UNESP]Gomes, Ana Carolina [UNESP]Bernardo, Nariane [UNESP]Carmo, Alisson Fernando [UNESP]Rodrigues, ThananWatanabe, Fernanda [UNESP]IEEE2019-10-04T12:32:40Z2019-10-04T12:32:40Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9300-9303Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 9300-9303, 2018.2153-6996http://hdl.handle.net/11449/185095WOS:00045103980821466913103944104900000-0002-8077-2865Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposiuminfo:eu-repo/semantics/openAccess2024-06-18T15:02:39Zoai:repositorio.unesp.br:11449/185095Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:51:38.849150Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
title PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
spellingShingle PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
Alcantara, Enner [UNESP]
Sentinel-3
C2RCC
inland water
IOPs
title_short PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
title_full PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
title_fullStr PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
title_full_unstemmed PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
title_sort PERFORMANCE ANALYSIS OF THE C2RCC PROCESSOR IN ESTIMATE THE WATER QUALITY PARAMETERS IN INLAND WATERS USING OLCl/SENTINEL-3A IMAGES
author Alcantara, Enner [UNESP]
author_facet Alcantara, Enner [UNESP]
Andrade, Caroline Pilfer de [UNESP]
Gomes, Ana Carolina [UNESP]
Bernardo, Nariane [UNESP]
Carmo, Alisson Fernando [UNESP]
Rodrigues, Thanan
Watanabe, Fernanda [UNESP]
IEEE
author_role author
author2 Andrade, Caroline Pilfer de [UNESP]
Gomes, Ana Carolina [UNESP]
Bernardo, Nariane [UNESP]
Carmo, Alisson Fernando [UNESP]
Rodrigues, Thanan
Watanabe, Fernanda [UNESP]
IEEE
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Fed Inst Educ Sci & Technol Para
dc.contributor.author.fl_str_mv Alcantara, Enner [UNESP]
Andrade, Caroline Pilfer de [UNESP]
Gomes, Ana Carolina [UNESP]
Bernardo, Nariane [UNESP]
Carmo, Alisson Fernando [UNESP]
Rodrigues, Thanan
Watanabe, Fernanda [UNESP]
IEEE
dc.subject.por.fl_str_mv Sentinel-3
C2RCC
inland water
IOPs
topic Sentinel-3
C2RCC
inland water
IOPs
description Remote sensing can be a powerful tool for long-teen spatial and temporal water quality monitoring if proper sets of algorithms are available. To estimate optically significant substances (OSS) by satellite images the water-leaving reflectance (pw) must be accurately estimated because it is directly related to the inherent optical properties (IOPs). For an accurate pw an effective atmospheric correction method must be used to remote the contribution of the atmospheric path radiance. The C2RCC processor has a set of algorithms capable of reduce the atmospheric path radiance, estimate the IOPs and then the OSS concentrations. But, the C2RCC was only tested using OL Cl/Sentine1-3 images for coastal areas, therefore, is of huge importance to know about their accuracy for inland waters. The results showed that the pw (with errors from 26.57 to 97.48%), IOPs (with errors from 39.77 to 99.90%) and OSS concentrations (with errors from 49.29 to 148.40%) estimated by C2RCC have no correlation with in situ data. For a long-teen use of OL Cl/Sentine1-3 images researchers must try to use another atmospheric correction and IOPs estimation methods when studying inland waters.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2019-10-04T12:32:40Z
2019-10-04T12:32:40Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 9300-9303, 2018.
2153-6996
http://hdl.handle.net/11449/185095
WOS:000451039808214
6691310394410490
0000-0002-8077-2865
identifier_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 9300-9303, 2018.
2153-6996
WOS:000451039808214
6691310394410490
0000-0002-8077-2865
url http://hdl.handle.net/11449/185095
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9300-9303
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
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reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
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instname_str Universidade Estadual Paulista (UNESP)
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