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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/48P6232
Repositorysid.inpe.br/mtc-m21d/2023/03.21.14.24
Last Update2023:03.21.14.24.49 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2023/03.21.14.24.49
Metadata Last Update2024:04.17.08.12.04 (UTC) administrator
DOI10.3390/rs15051299
ISSN2072-4292
Citation KeyLimaGiBrBaFaPeBe:2023:CoSeMa
TitleAssessment of estimated phycocyanin and chlorophyll-a concentration from PRISMA and OLCI in Brazilian inland waters: a comparison between semi-analytical and machine learning algorithms
Year2023
MonthMar.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4936 KiB
2. Context
Author1 Lima, Thainara Munhoz Alexandre de
2 Giardino, Claudia
3 Bresciani, Mariano
4 Barbosa, Cláudio Clemente Faria
5 Fabbretto, Alice
6 Pellegrino, Andrea
7 Begliomini, Felipe Nincao
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JGSB
ORCID1 0000-0001-6492-0330
2 0000-0002-3937-4988
3 0000-0002-7185-8464
4 0000-0002-3221-9774
5
6 0000-0002-4152-3409
7 0000-0001-8008-941X
Group1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
2
3
4 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 National Research Council of Italy
3 National Research Council of Italy
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 National Research Council of Italy
6 National Research Council of Italy
7 University of Cambridge
Author e-Mail Address1 thaimunhoz98@gmail.com
2
3
4 claudio.barbosa@inpe.br
JournalRemote Sensing
Volume15
Number5
Pagese1299
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2023-03-21 14:24:49 :: simone -> administrator ::
2023-03-21 14:24:51 :: administrator -> simone :: 2023
2023-03-21 14:25:28 :: simone -> administrator :: 2023
2023-04-17 14:52:00 :: administrator -> simone :: 2023
2023-06-22 15:57:35 :: simone -> administrator :: 2023
2024-04-17 08:12:04 :: administrator -> simone :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsaquatic remote sensing
cyanobacteria
hyperspectral
machine learning
phycocyanin
semi-analytical model
AbstractThe aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance ((Formula presented.)), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the (Formula presented.) spectra provided by PRISMA L2C productsand those derived with ACOLITE, in the case of OLCIas these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Assessment of estimated...
Arrangement 2urlib.net > BDMCI > Fonds > LabISA > Assessment of estimated...
Arrangement 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Assessment of estimated...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34T/48P6232
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34T/48P6232
Languageen
Target Fileremotesensing-15-01299-v2.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/439EAFB
8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 6
sid.inpe.br/mtc-m21/2012/07.13.14.43.57 3
sid.inpe.br/bibdigital/2022/04.03.22.23 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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