%0 Journal Article
%2 sid.inpe.br/mtc-m21c/2021/08.03.12.49.36
%4 sid.inpe.br/mtc-m21c/2021/08.03.12.49
%3 silva_machine_2021.pdf
%8 Aug.
%9 journal article
%@issn 2352-9385
%A Silva, Edson Filisbino Freire da,
%A Novo, Evlyn Márcia Leão de Moraes,
%A Lobo, Felipe de Lucia,
%A Barbosa, Cláudio Clemente Faria,
%A Cairo, Carolline Tressmann,
%A Noernberg, Maurício Almeida,
%A Rotta, Luiz Henrique da Silva,
%@secondarytype PRE PI
%B Remote Sensing Applications: Society and Environment
%D 2021
%K Classification, Machine learning, Novelty detection, Optical water type.
%P e100577
%T A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI
%V 23
%X 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.
%@area SRE
%@electronicmailaddress edson.freirefs@gmail.com
%@electronicmailaddress evlyn.novo@inpe.br
%@electronicmailaddress
%@electronicmailaddress claudio.barbosa@inpe.br
%@electronicmailaddress carolline.cairo@inpe.br
%@documentstage not transferred
%@group SER-SRE-DIPGR-INPE-MCTI-GOV-BR
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@group
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@group SER-SRE-DIPGR-INPE-MCTI-GOV-BR
%@dissemination PORTALCAPES; SCOPUS.
%@orcid 0000-0002-1097-9801
%@usergroup simone
%@nexthigherunit 8JMKD3MGPCW/3F3NU5S
%@nexthigherunit 8JMKD3MGPCW/439EAFB
%@nexthigherunit 8JMKD3MGPCW/46KUATE
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JH39
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JGSB
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal de Pelotas (UFPel)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal do Paraná (UFPR)
%@affiliation Universidade Estadual Paulista (UNESP)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1016/j.rsase.2021.100577