Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties
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/rs10101655 http://hdl.handle.net/11449/188252 |
Resumo: | The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (r) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s-1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral r achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors. |
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Repositório Institucional da UNESP |
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spelling |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical PropertiesInland watersOptically complex systemsRemote sensing accuracyThe quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (r) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s-1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral r achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Cartography São Paulo State University-UNESPDepartment of Environmental Engineering São Paulo State University-UNESPFederal Institute of Education Science and Technology of Pará State-IFPADepartment of Cartography São Paulo State University-UNESPDepartment of Environmental Engineering São Paulo State University-UNESPFAPESP: 2012/19821-1Universidade Estadual Paulista (Unesp)Science and Technology of Pará State-IFPABernardo, Nariane [UNESP]Alcântara, Enner [UNESP]Watanabe, Fernanda [UNESP]Rodrigues, ThananCarmo, Alisson [UNESP]Gomes, Ana [UNESP]Andrade, Caroline [UNESP]2019-10-06T16:02:07Z2019-10-06T16:02:07Z2018-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/rs10101655Remote Sensing, v. 10, n. 10, 2018.2072-4292http://hdl.handle.net/11449/18825210.3390/rs101016552-s2.0-8505542851866913103944104900000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:01:27Zoai:repositorio.unesp.br:11449/188252Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:46:31.438261Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
title |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
spellingShingle |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties Bernardo, Nariane [UNESP] Inland waters Optically complex systems Remote sensing accuracy |
title_short |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
title_full |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
title_fullStr |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
title_full_unstemmed |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
title_sort |
Glint removal assessment to estimate the remote sensing reflectance in inland waters withwidely differing optical Properties |
author |
Bernardo, Nariane [UNESP] |
author_facet |
Bernardo, Nariane [UNESP] Alcântara, Enner [UNESP] Watanabe, Fernanda [UNESP] Rodrigues, Thanan Carmo, Alisson [UNESP] Gomes, Ana [UNESP] Andrade, Caroline [UNESP] |
author_role |
author |
author2 |
Alcântara, Enner [UNESP] Watanabe, Fernanda [UNESP] Rodrigues, Thanan Carmo, Alisson [UNESP] Gomes, Ana [UNESP] Andrade, Caroline [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Science and Technology of Pará State-IFPA |
dc.contributor.author.fl_str_mv |
Bernardo, Nariane [UNESP] Alcântara, Enner [UNESP] Watanabe, Fernanda [UNESP] Rodrigues, Thanan Carmo, Alisson [UNESP] Gomes, Ana [UNESP] Andrade, Caroline [UNESP] |
dc.subject.por.fl_str_mv |
Inland waters Optically complex systems Remote sensing accuracy |
topic |
Inland waters Optically complex systems Remote sensing accuracy |
description |
The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (r) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s-1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral r achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-01 2019-10-06T16:02:07Z 2019-10-06T16:02:07Z |
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/rs10101655 Remote Sensing, v. 10, n. 10, 2018. 2072-4292 http://hdl.handle.net/11449/188252 10.3390/rs10101655 2-s2.0-85055428518 6691310394410490 0000-0002-8077-2865 |
url |
http://dx.doi.org/10.3390/rs10101655 http://hdl.handle.net/11449/188252 |
identifier_str_mv |
Remote Sensing, v. 10, n. 10, 2018. 2072-4292 10.3390/rs10101655 2-s2.0-85055428518 6691310394410490 0000-0002-8077-2865 |
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.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_ |
1808128855156719616 |