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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/42JUJ7B
Repositorysid.inpe.br/mtc-m21c/2020/06.02.13.00
Last Update2020:06.02.13.00.32 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/06.02.13.00.32
Metadata Last Update2021:03.29.12.54.46 (UTC) administrator
DOI10.3390/RS12081284
ISSN2072-4292
Citation KeySanchezIpiaPCACLSMSFQ:2020:CoClCo
TitleComparison of cloud cover detection algorithms on sentinel-2 images of the Amazon tropical forest
Year2020
MonthApr.
Access Date2024, Apr. 29
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size13957 KiB
2. Context
Author 1 Sanchez Ipia, Alber Hamersson
 2 Picoli, Michelle Cristina Araújo
 3 Câmara, Gilberto
 4 Andrade Neto, Pedro Ribeiro de
 5 Chaves, Michel Eustáquio Dantas
 6 Lechler, Sarah
 7 Soares, Anderson Reis
 8 Marujo, Rennan de Freitas Bezerra
 9 Simões, Rolf Ezequiel de Oliveira
10 Ferreira, Karine Reis
11 Queiroz, Gilberto Ribeiro
Resume Identifier 1
 2
 3 8JMKD3MGP5W/3C9JHB8
 4
 5
 6
 7
 8
 9
10 8JMKD3MGP5W/3C9JHKN
Group 1 COCST-COCST-INPE-MCTIC-GOV-BR
 2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
 3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
 4 COCST-COCST-INPE-MCTIC-GOV-BR
 5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
 6
 7 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
 8 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
 9 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
10 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
11 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation 1 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 University of Münster
 7 Instituto Nacional de Pesquisas Espaciais (INPE)
 8 Instituto Nacional de Pesquisas Espaciais (INPE)
 9 Instituto Nacional de Pesquisas Espaciais (INPE)
10 Instituto Nacional de Pesquisas Espaciais (INPE)
11 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address 1 alber.ipia@inpe.br
 2 michelle.picoli@inpe.br
 3 gilberto.camara@inpe.br
 4 pedro.andrade@inpe.br
 5 michel.chaves@inpe.br
 6 s.lechler@uni-muenster.de
 7 anderson.soares@inpe.br
 8 renan.marujo@inpe.br
 9 rolf.simoes@inpe.br
10 karine.ferreira@inpe.br
11 gilberto.queiroz@inpe.br
JournalRemote Sensing
Volume12
Number8
Pagese1284
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2020-06-02 13:00:32 :: simone -> administrator ::
2020-06-02 13:00:32 :: administrator -> simone :: 2020
2020-06-02 13:01:47 :: simone -> administrator :: 2020
2020-06-05 14:11:13 :: administrator -> simone :: 2020
2020-06-23 22:49:33 :: simone -> administrator :: 2020
2020-07-08 17:10:56 :: administrator -> simone :: 2020
2020-12-14 14:35:33 :: simone -> administrator :: 2020
2021-03-29 12:54:46 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsremote sensing
amazon forest
clouds
Sentinel–2
Fmask
Sen2Cor
MAJA
s2cloudless
AbstractTropical forests regulate the global water and carbon cycles and also host most of the worlds biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel2 images. To achieve this, we tested four cloud detection algorithms on Sentinel2 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cors (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producers accuracy should consider its use.
AreaCST
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Comparison of cloud...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Comparison of cloud...
Arrangement 3urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Comparison of cloud...
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agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/42JUJ7B
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/42JUJ7B
Languageen
Target Filesanchez_comparison.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3T29H
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 4
sid.inpe.br/bibdigital/2013/10.19.20.40 2
sid.inpe.br/bibdigital/2013/09.13.21.11 2
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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