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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3TSAR2P
Repositorysid.inpe.br/mtc-m21c/2019/08.20.13.20
Last Update2019:08.20.13.20.43 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2019/08.20.13.20.43
Metadata Last Update2020:12.13.19.09.58 (UTC) administrator
DOI10.3390/rs11151744
ISSN2072-4292
Citation KeyMacielNoCaBaFlLo:2019:MuAp
TitleRetrieving total and inorganic suspended sediments in Amazon floodplain lakes: a multisensor approach
Year2019
MonthAug.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size8293 KiB
2. Context
Author1 Maciel, Daniel Andrade
2 Novo, Evlyn Márcia Leão de Moraes
3 Carvalho, Lino Augusto Sander de
4 Barbosa, Cláudio Clemente Faria
5 Flores Júnior, Rogério
6 Lobo, Felipe de Lucia
Resume Identifier1
2 8JMKD3MGP5W/3C9JH39
3
4 8JMKD3MGP5W/3C9JGSB
ORCID1 0000-0003-4543-5908
2 0000-0002-1223-9276
3 0000-0001-8839-5366
4 0000-0002-3221-9774
5
6 0000-0001-8061-0076
Group1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
3
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
6 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Federal do Rio de Janeiro (UFRJ)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 daniel.maciel@inpe.br
2 evlyn.novo@inpe.br
3
4 claudio.barbosa@inpe.br
JournalRemote Sensing
Volume11
Number15
Pagese1744
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2019-08-20 13:20:43 :: simone -> administrator ::
2019-08-20 13:20:43 :: administrator -> simone :: 2019
2019-08-20 13:21:24 :: simone -> administrator :: 2019
2020-12-13 19:09:58 :: administrator -> simone :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordssuspended sediments
Amazon Floodplains
Optically Complex Waters
Monte Carlo Simulation
inorganic sediments
AbstractRemote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Retrieving total and...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Retrieving total and...
Arrangement 3urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Retrieving total and...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/3TSAR2P
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/3TSAR2P
Languageen
Target Fileremotesensing-11-01744.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 6
sid.inpe.br/mtc-m21/2012/07.13.14.43.57 3
sid.inpe.br/bibdigital/2013/10.18.22.34 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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