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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3R8FJDS
Repositorysid.inpe.br/mtc-m21c/2018/06.04.18.31   (restricted access)
Last Update2018:06.04.18.31.52 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2018/06.04.18.31.52
Metadata Last Update2024:01.23.13.41.07 (UTC) simone
DOI10.1016/j.jag.2018.03.005
ISSN0303-2434
Citation KeyChenLMBDSSHLO:2018:MaCrCr
TitleMapping croplands, cropping patterns, and crop types using MODIS time-series data
Year2018
MonthJuly
Access Date2024, Apr. 26
Secondary TypePRE PI
Number of Files1
Size2592 KiB
2. Context
Author 1 Chen, Yaoliang
 2 Lu, Dengsheng
 3 Moran, Emilio
 4 Batistella, Mateus
 5 Dutra, Luciano Vieira
 6 Sanches, Ieda Del'Arco
 7 Silva, Ramon Felipe Bicudo da
 8 Huang, Jingfeng
 9 Luiz, Alfredo José Barreto
10 Oliveira, Maria Antonia Falcão de
Resume Identifier 1
 2
 3
 4
 5 8JMKD3MGP5W/3C9JHMA
Group 1
 2
 3
 4
 5 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
 7
 8
 9
10 CGOBT-CGOBT-INPE-MCTIC-GOV-BR
Affiliation 1 Zhejiang Agriculture and Forestry University
 2 Zhejiang Agriculture and Forestry University
 3 Michigan State Universit
 4 Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
 5 Instituto Nacional de Pesquisas Espaciais (INPE)
 6 Instituto Nacional de Pesquisas Espaciais (INPE)
 7 Universidade Estadual de Campinas (UNICAMP)
 8 Zhejiang University
 9 Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
10 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address 1 chengis0115@gmail.com
 2 luds@zafu.edu.cn
 3 moranef@msu.edu
 4 mateus.batistella@embrapa.br
 5 dutra@dpi.inpe.br
 6 ieda.sanches@inpe.br
 7 ramonbicudo@gmail.com
 8 hjf@zju.edu.cn
 9 alfredo.luiz@embrapa.br
10 marian.florestal@gmail.com
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume69
Pages133-147
Secondary MarkB1_GEOCIÊNCIAS
History (UTC)2018-06-04 18:31:52 :: simone -> administrator ::
2018-06-04 18:31:53 :: administrator -> simone :: 2018
2018-06-04 18:36:09 :: simone -> administrator :: 2018
2019-01-04 16:57:05 :: administrator -> simone :: 2018
2019-01-07 11:15:38 :: simone -> administrator :: 2018
2020-12-07 21:11:47 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsCroplands
Cropping patterns
Crop types
MODIS NDVI
Decision tree classifier
Brazil
AbstractThe importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.
AreaSRE
Arrangement 1Projeto Memória 60... > DIDPI > Mapping croplands, cropping...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Mapping croplands, cropping...
Arrangement 3urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CGOBT > Mapping croplands, cropping...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Filechen_mapping.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3EU2H28
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 2
sid.inpe.br/mtc-m21/2012/07.13.14.53.50 1
sid.inpe.br/bibdigital/2013/10.01.23.43 1
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
NotesPrêmio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura sustentável
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
7. Description control
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