Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications

The Guadalquivir estuary (southern Spain) occasionally experiences medium to high turbidity, reaching above 700 Formazin Nephelometric Unit (FNU) during extreme events, thus negatively influencing its nursery function and the estuarine community structure. Although several turbidity algorithms are a...

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Authors: Chowdhury, Masuma, Vilas, César, Bergeijk, Stefanie Anne van, Navarro, Gabriel, Laiz, Irene, Caballero, Isabel
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/339754
Online Access:http://hdl.handle.net/10261/339754
https://api.elsevier.com/content/abstract/scopus_id/85167427597
Access Level:Open access
Keyword:Atmospheric correction
Ecosystem management
Guadalquivir estuary
Multi-conditional algorithm
Sentinel-2
Turbidity
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network_name_str España
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dc.title.none.fl_str_mv Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
title Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
spellingShingle Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
Chowdhury, Masuma
Atmospheric correction
Ecosystem management
Guadalquivir estuary
Multi-conditional algorithm
Sentinel-2
Turbidity
title_short Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
title_full Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
title_fullStr Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
title_full_unstemmed Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
title_sort Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications
dc.creator.none.fl_str_mv Chowdhury, Masuma
Vilas, César
Bergeijk, Stefanie Anne van
Navarro, Gabriel
Laiz, Irene
Caballero, Isabel
author Chowdhury, Masuma
author_facet Chowdhury, Masuma
Vilas, César
Bergeijk, Stefanie Anne van
Navarro, Gabriel
Laiz, Irene
Caballero, Isabel
author_role author
author2 Vilas, César
Bergeijk, Stefanie Anne van
Navarro, Gabriel
Laiz, Irene
Caballero, Isabel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Junta de Andalucía
AZTI-Tecnalia
Organismo Autónomo Parques Nacionales (España)
Sistema d’observació i predicció costaner de les Illes Balears
Universidad de Cádiz
Universidad de Vigo
Consejo Superior de Investigaciones Científicas (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Atmospheric correction
Ecosystem management
Guadalquivir estuary
Multi-conditional algorithm
Sentinel-2
Turbidity
topic Atmospheric correction
Ecosystem management
Guadalquivir estuary
Multi-conditional algorithm
Sentinel-2
Turbidity
description The Guadalquivir estuary (southern Spain) occasionally experiences medium to high turbidity, reaching above 700 Formazin Nephelometric Unit (FNU) during extreme events, thus negatively influencing its nursery function and the estuarine community structure. Although several turbidity algorithms are available to monitor water quality, they are mainly developed for mapping turbidity ranges of 0-100 FNU. Thus, their use in a highly turbid region may not give accurate results, which is crucial for estuarine ecosystem management. To fill this gap, we developed a multi-conditional turbidity algorithm that can retrieve turbidity from 0 to 600 FNU using the Sentinel-2 red and red-edge bands. Four major steps are implemented: atmospheric and sun glint correction of the Level-1C Sentinel-2 data, spectral analysis for different water turbidity levels, regression modelling between in situ turbidity and remote sensing reflectance (Rrs) for algorithm development, and validation of the best-suited model. When turbidity was < 85 FNU, the Rrs increased firstly in the red wavelength (665 nm), but it saturated beyond a certain turbidity threshold (> 250 FNU). At this time, Rrs started to increase in the red-edge wavelength (704 nm). Considering this spectral behavior, our algorithm is designed to automatically select the most sensitive turbidity vs. Rrs, thus avoiding the saturation effects of the red bands at high turbidity levels. The model showed good agreement between the satellite derived turbidity and the in situ measurements with a correlation coefficient of 0.97, RMSE of 15.93 FNU, and a bias of 13.34 FNU. Turbidity maps derived using this algorithm can be used for routine turbidity monitoring and assessment of potential anthropogenic actions (e.g., dredging activities), thus helping the decision-makers and relevant stakeholders to protect coastal resources and human health.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/339754
https://api.elsevier.com/content/abstract/scopus_id/85167427597
url http://hdl.handle.net/10261/339754
https://api.elsevier.com/content/abstract/scopus_id/85167427597
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098784-J-I00
info:eu-repo/grantAgreement/AEI//IJC2019-039382-I
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3389/fmars.2023.1186441
https://doi.org/10.3389/fmars.2023.1186441
Sí
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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spelling Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applicationsChowdhury, MasumaVilas, CésarBergeijk, Stefanie Anne vanNavarro, GabrielLaiz, IreneCaballero, IsabelAtmospheric correctionEcosystem managementGuadalquivir estuaryMulti-conditional algorithmSentinel-2TurbidityThe Guadalquivir estuary (southern Spain) occasionally experiences medium to high turbidity, reaching above 700 Formazin Nephelometric Unit (FNU) during extreme events, thus negatively influencing its nursery function and the estuarine community structure. Although several turbidity algorithms are available to monitor water quality, they are mainly developed for mapping turbidity ranges of 0-100 FNU. Thus, their use in a highly turbid region may not give accurate results, which is crucial for estuarine ecosystem management. To fill this gap, we developed a multi-conditional turbidity algorithm that can retrieve turbidity from 0 to 600 FNU using the Sentinel-2 red and red-edge bands. Four major steps are implemented: atmospheric and sun glint correction of the Level-1C Sentinel-2 data, spectral analysis for different water turbidity levels, regression modelling between in situ turbidity and remote sensing reflectance (Rrs) for algorithm development, and validation of the best-suited model. When turbidity was < 85 FNU, the Rrs increased firstly in the red wavelength (665 nm), but it saturated beyond a certain turbidity threshold (> 250 FNU). At this time, Rrs started to increase in the red-edge wavelength (704 nm). Considering this spectral behavior, our algorithm is designed to automatically select the most sensitive turbidity vs. Rrs, thus avoiding the saturation effects of the red bands at high turbidity levels. The model showed good agreement between the satellite derived turbidity and the in situ measurements with a correlation coefficient of 0.97, RMSE of 15.93 FNU, and a bias of 13.34 FNU. Turbidity maps derived using this algorithm can be used for routine turbidity monitoring and assessment of potential anthropogenic actions (e.g., dredging activities), thus helping the decision-makers and relevant stakeholders to protect coastal resources and human health.This research was partly funded by grants RTI2018-098784-JI00 (Sen2Coast Project) and IJC2019-039382-I (Juan de la Cierva-Incorporación) from the MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. The research was also supported by the Andalusia Regional Government (PY20-00244), National Project OAPN (Observatorio TIAMAT, REF: 2715/2021) and the European Union-NextGenerationEU Agreement between MITECO, CSIC, AZTI, SOCIB, and the universities of Vigo and Cadiz, to promote research and generate scientific knowledge in the field of marine sustainability. Estuary monitoring and in situ data were provided by IFAPA-Junta de Andalucıá projects GUADALQUIVIR_LTER-PP.FEM. PPA201700.5 and GUADACONECT-PR.FEM.PPA201900.005, 75% co-funded by the European Maritime and Fisheries Fund (EMFF) 2014-2020. The three field campaigns were supported by the Spanish Ministerio de Ciencia e Innovación, the Agencia Estatal de Investigación, and the European Regional Development Fund in the frame of the Sen2Coast Project. MC is a PhD student at the University of Cadiz who is currently employed by the company Quasar Science Resources S.L. Consequently, MC is 50% funded by Quasar and 50% by the Industrial Doctorate Program of the Spanish Ministerio de Ciencia e Innovación (ref. DIN2020-010979/AEI/10.13039/501100011033). This work is part of MC’s PhD within the SIMBAD project (ref. QSR-ESABIC-2018-001, incubated by ESA-BIC Madrid region) and the University of Cadiz, and was partly supported by a grant funded by the European Commission under the Erasmus Mundus Joint Master Degree Programme in Water and Coastal Management (WACOMA; Project num. 586596-EPP-1-2017-1-IT-EPPKA1-JMD-MOB) and represents a contribution to CSIC Thematic Interdisciplinary Platform PTI TELEDETECT and PTI Oceans+.The open access fee was co-funded by the QUALIFICA Project (QUAL21-0019, Junta de Andalucía).Peer reviewedFrontiers MediaMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionJunta de AndalucíaAZTI-TecnaliaOrganismo Autónomo Parques Nacionales (España)Sistema d’observació i predicció costaner de les Illes BalearsUniversidad de CádizUniversidad de VigoConsejo Superior de Investigaciones Científicas (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/339754https://api.elsevier.com/content/abstract/scopus_id/85167427597reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098784-J-I00info:eu-repo/grantAgreement/AEI//IJC2019-039382-IThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3389/fmars.2023.1186441https://doi.org/10.3389/fmars.2023.1186441Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3397542026-05-22T06:33:51Z
score 15,81155

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