| 1. Identificação | ||||
| Tipo de Referência | Artigo em Revista Científica (Journal Article) | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34R/457CSLS | |||
| Repositório | sid.inpe.br/mtc-m21c/2021/08.03.12.49 (acesso restrito) | |||
| Última Atualização | 2021:08.03.12.49.36 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21c/2021/08.03.12.49.36 | |||
| Última Atualização dos Metadados | 2022:04.03.22.28.49 (UTC) administrator | |||
| DOI | 10.1016/j.rsase.2021.100577 | |||
| ISSN | 2352-9385 | |||
| Chave de Citação | SilvaNoLoBaCaNoRo:2021:MaLeAp | |||
| Título | A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI  | |||
| Ano | 2021 | |||
| Mês | Aug. | |||
| Data de Acesso | 31 out. 2024 | |||
| Tipo de Trabalho | journal article | |||
| Tipo Secundário | PRE PI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 7823 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Silva, Edson Filisbino Freire da 2 Novo, Evlyn Márcia Leão de Moraes 3 Lobo, Felipe de Lucia 4 Barbosa, Cláudio Clemente Faria 5 Cairo, Carolline Tressmann 6 Noernberg, Maurício Almeida 7 Rotta, Luiz Henrique da Silva | |||
| Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JH39 3 4 8JMKD3MGP5W/3C9JGSB | |||
| ORCID | 1 0000-0002-1097-9801 | |||
| Grupo | 1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 4 DIOTG-CGCT-INPE-MCTI-GOV-BR 5 SER-SRE-DIPGR-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade Federal de Pelotas (UFPel) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Universidade Federal do Paraná (UFPR) 7 Universidade Estadual Paulista (UNESP) | |||
| Endereço de e-Mail do Autor | 1 edson.freirefs@gmail.com 2 evlyn.novo@inpe.br 3 4 claudio.barbosa@inpe.br 5 carolline.cairo@inpe.br | |||
| Revista | Remote Sensing Applications: Society and Environment | |||
| Volume | 23 | |||
| Páginas | e100577 | |||
| Histórico (UTC) | 2021-08-03 12:49:36 :: simone -> administrator :: 2022-04-03 22:28:49 :: administrator -> simone :: 2021 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Classification Machine learning Novelty detection Optical water type | |||
| Resumo | Optical 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. | |||
| Área | SRE | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Idioma | en | |||
| Arquivo Alvo | silva_machine_2021.pdf | |||
| Grupo de Usuários | simone | |||
| Visibilidade | shown | |||
| Permissão de Leitura | deny from all and allow from 150.163 | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE | |||
| Divulgação | PORTALCAPES; SCOPUS. | |||
| Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||