Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IGARSS.2018.8517486 http://hdl.handle.net/11449/188855 |
Resumo: | Remote sensing can be a powerful tool for long-term 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 OLCI/Sentinel-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-term use of OLCI/Sentinel-3 images researchers must try to use another atmospheric correction and IOPs estimation methods when studying inland waters. |
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Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A imagesC2RCCInland waterIOPsSentinel-3Remote sensing can be a powerful tool for long-term 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 OLCI/Sentinel-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-term use of OLCI/Sentinel-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)Department of Environmental Engineering São Paulo State University - UnespDepartment of Cartography São Paulo State University - UnespFederal Institute of Education Science and Technology from ParáDepartment of Environmental Engineering São Paulo State University - UnespDepartment of Cartography São Paulo State University - UnespFAPESP: 2015/21586-9Universidade Estadual Paulista (Unesp)Science and Technology from ParáAlcântara, Enner [UNESP]De Andrade, Caroline Piffer [UNESP]Gomes, Ana Carolina [UNESP]Bernardo, Nariane [UNESP]Carmo, Alisson Fernando [UNESP]Rodrigues, ThananWatanabe, Fernanda [UNESP]2019-10-06T16:21:22Z2019-10-06T16:21:22Z2018-10-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9300-9303http://dx.doi.org/10.1109/IGARSS.2018.8517486International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 9300-9303.http://hdl.handle.net/11449/18885510.1109/IGARSS.2018.85174862-s2.0-8506313775066913103944104900000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Geoscience and Remote Sensing Symposium (IGARSS)info:eu-repo/semantics/openAccess2024-06-18T15:02:08Zoai:repositorio.unesp.br:11449/188855Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-18T15:02:08Repositório 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 OLCI/Sentinel-3A images |
title |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
spellingShingle |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images Alcântara, Enner [UNESP] C2RCC Inland water IOPs Sentinel-3 |
title_short |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
title_full |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
title_fullStr |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
title_full_unstemmed |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
title_sort |
Performance analysis of the C2RCC processor in estimate the water quality parameters in inland waters using OLCI/Sentinel-3A images |
author |
Alcântara, Enner [UNESP] |
author_facet |
Alcântara, Enner [UNESP] De Andrade, Caroline Piffer [UNESP] Gomes, Ana Carolina [UNESP] Bernardo, Nariane [UNESP] Carmo, Alisson Fernando [UNESP] Rodrigues, Thanan Watanabe, Fernanda [UNESP] |
author_role |
author |
author2 |
De Andrade, Caroline Piffer [UNESP] Gomes, Ana Carolina [UNESP] Bernardo, Nariane [UNESP] Carmo, Alisson Fernando [UNESP] Rodrigues, Thanan Watanabe, Fernanda [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Science and Technology from Pará |
dc.contributor.author.fl_str_mv |
Alcântara, Enner [UNESP] De Andrade, Caroline Piffer [UNESP] Gomes, Ana Carolina [UNESP] Bernardo, Nariane [UNESP] Carmo, Alisson Fernando [UNESP] Rodrigues, Thanan Watanabe, Fernanda [UNESP] |
dc.subject.por.fl_str_mv |
C2RCC Inland water IOPs Sentinel-3 |
topic |
C2RCC Inland water IOPs Sentinel-3 |
description |
Remote sensing can be a powerful tool for long-term 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 OLCI/Sentinel-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-term use of OLCI/Sentinel-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 |
2018-10-31 2019-10-06T16:21:22Z 2019-10-06T16:21:22Z |
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 |
http://dx.doi.org/10.1109/IGARSS.2018.8517486 International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 9300-9303. http://hdl.handle.net/11449/188855 10.1109/IGARSS.2018.8517486 2-s2.0-85063137750 6691310394410490 0000-0002-8077-2865 |
url |
http://dx.doi.org/10.1109/IGARSS.2018.8517486 http://hdl.handle.net/11449/188855 |
identifier_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 9300-9303. 10.1109/IGARSS.2018.8517486 2-s2.0-85063137750 6691310394410490 0000-0002-8077-2865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9300-9303 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1803045211049295872 |