Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/rs10020227 http://hdl.handle.net/11449/175936 |
Resumo: | The high nutrient concentrations coming from non-point and point pollution have been linked to algae blooms, especially in hydroelectric plant reservoirs, due to higher residence time compared to rivers. The monitoring of algae is important to prevent risk of contamination by toxins in reservoirs used for drinking water supply. In this context, a physical model-based approach was adopted to retrieve chlorophyll-a (chl a) concentration, a photosynthetic pigment found in all phytoplankton species. We assumed that a semi-analytical algorithm parameterized to a eutrophic reservoir could also be applied to other eutrophic reservoirs, at least the specific inherent optical properties (SIOPs) are not similar. The parameterization was carried out based on Ocean and Land Color Instrument (OLCI) bands aboard Sentinel-3 spacecraft. In our study, the semi-analytical approach showed good performance in retrieving chl a content, with a normalized root mean square error (NRMSE) of 18.7%. The findings encourage the use of a unique semi-analytical algorithm in a reservoir cascade, where the impoundments present similar bio-optical status. The good performance of the algorithm indicates that this approach is rather useful in predicting trophic status in reservoirs. |
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Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland watersOLCI Sentinel-3Productive inland watersSemi-analytical approachTrophic stateThe high nutrient concentrations coming from non-point and point pollution have been linked to algae blooms, especially in hydroelectric plant reservoirs, due to higher residence time compared to rivers. The monitoring of algae is important to prevent risk of contamination by toxins in reservoirs used for drinking water supply. In this context, a physical model-based approach was adopted to retrieve chlorophyll-a (chl a) concentration, a photosynthetic pigment found in all phytoplankton species. We assumed that a semi-analytical algorithm parameterized to a eutrophic reservoir could also be applied to other eutrophic reservoirs, at least the specific inherent optical properties (SIOPs) are not similar. The parameterization was carried out based on Ocean and Land Color Instrument (OLCI) bands aboard Sentinel-3 spacecraft. In our study, the semi-analytical approach showed good performance in retrieving chl a content, with a normalized root mean square error (NRMSE) of 18.7%. The findings encourage the use of a unique semi-analytical algorithm in a reservoir cascade, where the impoundments present similar bio-optical status. The good performance of the algorithm indicates that this approach is rather useful in predicting trophic status in reservoirs.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Cartography Faculty of Sciences and Technology São Paulo State University (UNESP), Rua Roberto Simonsen 305Department of Environmental Engineering Institute of Science and Technology São Paulo State University (UNESP), Rodovia Presidente Dutra Km 137.8Federal Institute of Education Science and Technology from Pará, Rodovia BR 316, km 61Department of Cartography Faculty of Sciences and Technology São Paulo State University (UNESP), Rua Roberto Simonsen 305Department of Environmental Engineering Institute of Science and Technology São Paulo State University (UNESP), Rodovia Presidente Dutra Km 137.8Universidade Estadual Paulista (Unesp)Science and Technology from ParáWatanabe, Fernanda [UNESP]Alcântara, Enner [UNESP]Imai, Nilton [UNESP]Rodrigues, ThananBernardo, Nariane [UNESP]2018-12-11T17:18:13Z2018-12-11T17:18:13Z2018-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.3390/rs10020227Remote Sensing, v. 10, n. 2, 2018.2072-4292http://hdl.handle.net/11449/17593610.3390/rs100202272-s2.0-850425254662-s2.0-85042525466.pdf66913103944104900000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing1,386info:eu-repo/semantics/openAccess2024-06-18T15:01:12Zoai:repositorio.unesp.br:11449/175936Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:06:08.632115Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
title |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
spellingShingle |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters Watanabe, Fernanda [UNESP] OLCI Sentinel-3 Productive inland waters Semi-analytical approach Trophic state |
title_short |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
title_full |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
title_fullStr |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
title_full_unstemmed |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
title_sort |
Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters |
author |
Watanabe, Fernanda [UNESP] |
author_facet |
Watanabe, Fernanda [UNESP] Alcântara, Enner [UNESP] Imai, Nilton [UNESP] Rodrigues, Thanan Bernardo, Nariane [UNESP] |
author_role |
author |
author2 |
Alcântara, Enner [UNESP] Imai, Nilton [UNESP] Rodrigues, Thanan Bernardo, Nariane [UNESP] |
author2_role |
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 |
Watanabe, Fernanda [UNESP] Alcântara, Enner [UNESP] Imai, Nilton [UNESP] Rodrigues, Thanan Bernardo, Nariane [UNESP] |
dc.subject.por.fl_str_mv |
OLCI Sentinel-3 Productive inland waters Semi-analytical approach Trophic state |
topic |
OLCI Sentinel-3 Productive inland waters Semi-analytical approach Trophic state |
description |
The high nutrient concentrations coming from non-point and point pollution have been linked to algae blooms, especially in hydroelectric plant reservoirs, due to higher residence time compared to rivers. The monitoring of algae is important to prevent risk of contamination by toxins in reservoirs used for drinking water supply. In this context, a physical model-based approach was adopted to retrieve chlorophyll-a (chl a) concentration, a photosynthetic pigment found in all phytoplankton species. We assumed that a semi-analytical algorithm parameterized to a eutrophic reservoir could also be applied to other eutrophic reservoirs, at least the specific inherent optical properties (SIOPs) are not similar. The parameterization was carried out based on Ocean and Land Color Instrument (OLCI) bands aboard Sentinel-3 spacecraft. In our study, the semi-analytical approach showed good performance in retrieving chl a content, with a normalized root mean square error (NRMSE) of 18.7%. The findings encourage the use of a unique semi-analytical algorithm in a reservoir cascade, where the impoundments present similar bio-optical status. The good performance of the algorithm indicates that this approach is rather useful in predicting trophic status in reservoirs. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:18:13Z 2018-12-11T17:18:13Z 2018-02-01 |
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/rs10020227 Remote Sensing, v. 10, n. 2, 2018. 2072-4292 http://hdl.handle.net/11449/175936 10.3390/rs10020227 2-s2.0-85042525466 2-s2.0-85042525466.pdf 6691310394410490 0000-0002-8077-2865 |
url |
http://dx.doi.org/10.3390/rs10020227 http://hdl.handle.net/11449/175936 |
identifier_str_mv |
Remote Sensing, v. 10, n. 2, 2018. 2072-4292 10.3390/rs10020227 2-s2.0-85042525466 2-s2.0-85042525466.pdf 6691310394410490 0000-0002-8077-2865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing 1,386 |
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 |
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 |
|
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1808128608165691392 |