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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3PFBCHB
Repositorysid.inpe.br/mtc-m21b/2017/08.18.17.12
Last Update2017:08.18.17.12.12 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21b/2017/08.18.17.12.12
Metadata Last Update2020:12.31.02.07.55 (UTC) administrator
DOI10.3390/rs9070644
ISSN2072-4292
Citation KeyJorgeBaCaAfLoNo:2017:SNSiRa
TitleSNR (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex Amazon lakes
Year2017
MonthJuly
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size9750 KiB
2. Context
Author1 Jorge, Daniel Schaffer Ferreira
2 Barbosa, Cláudio Clemente Faria
3 Carvalho, Lino Augusto Sander de
4 Affonso, Adriana Gomes
5 Lobo, Felipe de Lucia
6 Novo, Evlyn Márcia Leão de Moraes
Resume Identifier1
2 8JMKD3MGP5W/3C9JGSB
3
4
5
6 8JMKD3MGP5W/3C9JH39
Group1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
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 danielsfj@dsr.inpe.br
2 claudio.barbosa@inpe.br
3 lino@dsr.inpe.br
4 affonso@dsr.inpe.br
5 felipe.lobo@inpe.br
6 evlyn.novo@inpe.br
JournalRemote Sensing
Volume9
Number7
PagesArticle number 644
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2017-08-18 17:12:12 :: simone -> administrator ::
2017-08-18 17:12:12 :: administrator -> simone :: 2017
2017-08-18 17:12:54 :: simone -> administrator :: 2017
2017-08-29 17:27:17 :: administrator -> simone :: 2017
2017-12-14 16:39:32 :: simone -> administrator :: 2017
2020-12-31 02:07:55 :: administrator -> simone :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordssignal-to-noise ratio
Remote Sensing Reflectance
bio-optical algorithms
inland waters
AbstractUncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the Amazon floodplain lakes, such uncertainties are amplified due to their seasonal dynamic. Therefore, it is imperative to understand the suitability of a sensor to cope with them and assess their impact on the algorithms for the retrieval of constituents. The objective of this paper is to assess the impact of the SNR on the Chl-a and TSS algorithms in four lakes located at Mamirauá Sustainable Development Reserve (Amazonia, Brazil). Two data sets were simulated (noisy and noiseless spectra) based on in situ measurements and on sensor design (MSI/Sentinel-2, OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using three and four algorithms for TSS and Chl-a, respectively. The results showed that the impact of the SNR on each algorithm displayed similar patterns for both constituents. For additive and single band algorithms, the error amplitude is constant for the entire concentration range. However, for multiplicative algorithms, the error changes according to the model equation and the Rrs magnitude. Lastly, for the exponential algorithm, the retrieval amplitude is higher for a low concentration. The OLCI sensor has the best retrieval performance (error of up to 2 µg/L for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive and single band algorithms for TSS and Chl-a are low (up to 5 mg/L and 1 µg/L, respectively); but for the multiplicative algorithm, the errors were above 10 µg/L. The OLI simulation resulted in errors below 3 mg/L for TSS. However, the number and position of OLI bands restrict Chl-a retrieval. Sensor and algorithm selection need a comprehensive analysis of key factors such as sensor design, in situ conditions, water brightness (Rrs), and model equations before being applied for inland water studies.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > SNR (signal-to-noise ratio)...
Arrangement 2urlib.net > DIDSR > SNR (signal-to-noise ratio)...
Arrangement 3urlib.net > SER > SNR (signal-to-noise ratio)...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://mtc-m21b.sid.inpe.br/ibi/8JMKD3MGP3W34P/3PFBCHB
zipped data URLhttp://mtc-m21b.sid.inpe.br/zip/8JMKD3MGP3W34P/3PFBCHB
Languageen
Target Filejorge_snr.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
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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 4
sid.inpe.br/bibdigital/2013/09.13.21.11 3
sid.inpe.br/mtc-m21/2012/07.13.14.45.43 3
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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