A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorNational Institute for Space Research-
Autor(es): dc.contributorFederal University of Pelotas-
Autor(es): dc.contributorFederal University of Paraná-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFilisbino Freire da Silva, Edson-
Autor(es): dc.creatorMárcia Leão de Moraes Novo, Evlyn-
Autor(es): dc.creatorde Lucia Lobo, Felipe-
Autor(es): dc.creatorClemente Faria Barbosa, Cláudio-
Autor(es): dc.creatorTressmann Cairo, Carolline-
Autor(es): dc.creatorAlmeida Noernberg, Mauricio-
Autor(es): dc.creatorHenrique da Silva Rotta, Luiz-
Data de aceite: dc.date.accessioned2025-08-21T19:26:42Z-
Data de disponibilização: dc.date.available2025-08-21T19:26:42Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.rsase.2021.100577-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229116-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229116-
Descrição: dc.descriptionOptical Water Type (OWT) is a useful parameter for assessing water quality changes related to different turbidity levels, trophic state and colored dissolved organic matter (CDOM) while also helpful for tuning chlorophyll-a algorithms. For this reason, interest in the satellite remote sensing of OWTs has recently increased in recent years. This study develops a machine learning method for monitoring Brazilian OWTs using the Sentinel-2 MSI, which can detect OWTs already assessed by field measurements and recognize new OWTs. The already assessed OWTs used for calibrating the machine learning algorithm are clear, moderate turbid, eutrophic turbid, eutrophic clear, hypereutrophic, CDOM richest, turbid, and very turbid waters. The classification method consists of two Support Vector Machines for classifying the known OWTs, while a novelty detection method based on sigmoid functions is used for assessing new OWTs. Results show the classification based on Sentinel-2 MSI bands simulated using field radiometric data is accurate (accuracy = 0.94). However, when radiometric errors are simulated, the accuracy significantly decreases to 0.75, 0.56, 0.45, and 0.37 as the mean absolute percent error increases to 10%, 20%, 30%, and 40%, respectively. Considering the errors retrieved when comparing the field and satellite measurements, the expected accuracy of Sentinel-2 MSI images is 0.78. In the satellite images, the novelty detection distinguishes new OWTs originated from the mixture among the known OWTs and a new OWT that was not part of the training database (clear blue waters). Two examples of time series in the Funil reservoir and the Curuai lake are used to show the applicability of monitoring OWTs. In the Funil reservoir, OWTs could indicate eutrophication and turbid changes caused by river inflow and sediment sinking. In the Curuai lake, OWTs could indicate areas susceptible to algae bloom and turbidity increases related to river inflow and particle resuspension. In the future, the proposed algorithm could be used for large-scale assessment of water quality degradation and supports rapid mitigation and recovery responses. For improving the classification accuracy, adjacency correction and more robust glint removal methods should be developed.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionRemote Sensing Division National Institute for Space Research-
Descrição: dc.descriptionCDTec Federal University of Pelotas-
Descrição: dc.descriptionImage Processing Division National Institute for Space Research-
Descrição: dc.descriptionCenter of Marine Studies Federal University of Paraná-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University-
Descrição: dc.descriptionFAPESP: 2008/56252–0-
Descrição: dc.descriptionFAPESP: 2012/19821–1-
Descrição: dc.descriptionFAPESP: 2013/09045–7-
Descrição: dc.descriptionFAPESP: 2014/23903–9-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing Applications: Society and Environment-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectNovelty detection-
Palavras-chave: dc.subjectOptical water type-
Título: dc.titleA machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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