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dc.contributor.authorMONTIBELLER, B.
dc.contributor.authorLUIZ, A. J. B.
dc.contributor.authorSANCHES, I. D. A.
dc.contributor.authorSILVEIRA, H. L. F. da
dc.date.accessioned2018-01-09T23:14:02Z-
dc.date.available2018-01-09T23:14:02Z-
dc.date.created2018-01-09
dc.date.issued2017
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18.,2017, Santos. Anais... Santos: Inpe, 2017. Trabalho: 59410.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1084705-
dc.descriptionRemote sensing data has been widely used worldwide to estimate crop field?s parameters such as area. For that purpose, we use automatic classification algorithms to identify different land uses and land covers (e.g. agricultural and native vegetation), groups of crops (e.g. annual and perennial crops) or crops species (e.g. maize, sugarcane or soybean). For agricultural applications, the ultimate goal is to be able to use remote sensing technology to map crops in the specie level, and then to monitor them. One essential input data used in the classifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetation indices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of one classifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. In other words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specie among others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maize crop. To accomplish that, spectral data (OLI/Landsat-8) were acquired from first and second harvest maize plots, cultivated over distinct management systems (irrigated and non-irrigated), along two agricultural crop years, (2014/2015 and 2015/2016). We concluded that maize fields harvested in different years, sowed in different seasons, irrigated or not, have a high temporal spectral variation, which cannot be associated with these known characteristics.
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectSurface reflectance
dc.subjectMultitemporal data
dc.subjectOli-Landsat-8
dc.subjectAgricultural monitoring
dc.subjectReflectância de superfície
dc.subjectDado multitemporal
dc.subjectMonitoramento agrícola
dc.titleAnálise da variabilidade espectro-temporal intraespecífica do milho.
dc.typeArtigo em anais e proceedings
dc.date.updated2018-03-12T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento remoto
dc.subject.thesagroMilho
dc.subject.nalthesaurusRemote sensing
dc.format.extent2p. 2011-2018.
riaa.ainfo.id1084705
riaa.ainfo.lastupdate2018-03-12 -03:00:00
dc.contributor.institutionBRUNO MONTIBELLER, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; IEDA DEL'ARCO SANCHES, INPE; HILTON LUIS FERRAZ DA SILVEIRA, CNPS.
Aparece nas coleções:Artigo em anais de congresso (CNPMA)

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