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		<doi>10.3390/rs12010040</doi>
		<issn>2072-4292</issn>
		<label>lattes: 1596449770636962 2 CairoBLNCMFSC:2020:HyChAl</label>
		<citationkey>CairoBLNCMFSC:2020:HyChAl</citationkey>
		<title>Hybrid chlorophyll-a algorithm for assessing trophic states of a tropical brazilian reservoir based on MSI/Sentinel-2 data</title>
		<year>2020</year>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Cairo, Carolline Tressmann,</author>
		<author>Barbosa, Cláudio Clemente Faria,</author>
		<author>Lobo, Felipe de Lucia,</author>
		<author>Novo, Evlyn Márcia Leão de Moraes,</author>
		<author>Carlos, Felipe Menino,</author>
		<author>Maciel, Daniel Andrade,</author>
		<author>Flores Junior, Rogério,</author>
		<author>Silva, Edson Filisbino Freire da,</author>
		<author>Curtarelli, Victor Pedroso,</author>
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		<orcid></orcid>
		<orcid>0000-0002-3221-9774</orcid>
		<orcid>0000-0001-8061-0076</orcid>
		<orcid>0000-0002-1223-9276</orcid>
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		<orcid>0000-0003-4543-5908</orcid>
		<group>SER-SRE-SESPG-INPE-MCTIC-GOV-BR</group>
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		<group>SER-SRE-SESPG-INPE-MCTIC-GOV-BR</group>
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		<group>DIDPI-CGOBT-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>carolline.cairo@inpe.br</electronicmailaddress>
		<electronicmailaddress>claudio.barbosa@inpe.br</electronicmailaddress>
		<electronicmailaddress>felipellobo@gmail.com</electronicmailaddress>
		<electronicmailaddress>evlyn.novo@inpe.br</electronicmailaddress>
		<electronicmailaddress>felipe.carlos@fatec.sp.gov.br</electronicmailaddress>
		<electronicmailaddress>damaciel.maciel@hotmail.com</electronicmailaddress>
		<electronicmailaddress>rogerio.floresjr@gmail.com</electronicmailaddress>
		<electronicmailaddress>edson.freirefs@gmail.com</electronicmailaddress>
		<electronicmailaddress>victor.curtarelli@gmail.com</electronicmailaddress>
		<journal>Remote Sensing</journal>
		<volume>12</volume>
		<number>1</number>
		<pages>e12010040</pages>
		<secondarymark>B3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I</secondarymark>
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		<contenttype>External Contribution</contenttype>
		<versiontype>publisher</versiontype>
		<keywords>Hybrid Chlorophyll-a Algorithm, monitoramento da qualidade da água, clorofila-a.</keywords>
		<abstract>Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (31000 mg/m3 ). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.8922.83 mg/m3 , class 2 varies 19.5187.63 mg/m3 , and class 3 varies 75.89938.97 mg/m3 . The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAMs optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.</abstract>
		<area>SRE</area>
		<language>en</language>
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		<dissemination>WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.</dissemination>
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