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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/42JUHP8
Repositorysid.inpe.br/mtc-m21c/2020/06.02.12.55
Last Update2020:06.02.12.55.14 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/06.02.12.55.14
Metadata Last Update2022:01.04.01.35.10 (UTC) administrator
DOI10.3390/rs12071152
ISSN2072-4292
Citation KeyAraiSaDuCaHoSh:2020:VeFrIm
TitleVegetation fraction images derived from PROBA-V data for rapid assessment of annual croplands in Brazil
Year2020
MonthApr.
Access Date2024, Apr. 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size18990 KiB
2. Context
Author1 Arai, Egídio
2 Sano, Edson Eyji
3 Dutra, Andeise Cerqueira
4 Cassol, Henrique Luis Godinho
5 Hoffmann, Tânia Beatriz
6 Shimabukuro, Yosio Edemir
Resume Identifier1 8JMKD3MGP5W/3C9JGUP
2
3
4
5
6 8JMKD3MGP5W/3C9JJCQ
Group1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
2
3 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
4 SER-SRE-SESPG-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 Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
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 egidio@dsr.inpe.br
2 edson.sano@embrapa.br
3 andeise.dutra@inpe.br
4 henrique@dsr.inpe.br
5 tania.hoffmann@inpe.br
6 yosio@dsr.inpe.br
JournalRemote Sensing
Volume12
Number7
Pagese1152
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2020-06-02 12:55:14 :: simone -> administrator ::
2020-06-02 12:55:15 :: administrator -> simone :: 2020
2020-06-02 12:55:45 :: simone -> administrator :: 2020
2020-06-07 08:43:42 :: administrator -> simone :: 2020
2020-06-23 22:40:10 :: simone -> administrator :: 2020
2022-01-04 01:35:10 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywords: linear spectral mixing model
Mato Grosso State
cropland mapping
maximum fraction values mosaic
AbstractThis paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and MonitoringGlobal Land Cover (FROM-GLC) and Global Food SecuritySupport Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Vegetation fraction images...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Vegetation fraction images...
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/8JMKD3MGP3W34R/42JUHP8
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/42JUHP8
Languageen
Target Filearai_remote sensing.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/mtc-m21/2012/07.13.14.45.03 2
sid.inpe.br/bibdigital/2013/09.13.21.11 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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