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		<isbn>978-85-17-00088-1</isbn>
		<label>59410</label>
		<citationkey>MontibellerLuizSancSilv:2017:AnVaEs</citationkey>
		<title>Análise da variabilidade espectro-temporal intraespecífica do milho</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Montibeller, Bruno,</author>
		<author>Luiz, Alfredo José Barreto,</author>
		<author>Sanches, Ieda Del Arco,</author>
		<author>Silveira, Hilton Luís Ferraz da,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation></affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>bruno.montibeller@inpe.br</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>2011-2018</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
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		<abstract>Remote sensing data has been widely used worldwide to estimate crop fields 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 sensingtechnology to map crops in the specie level, and then to monitor them. One essential input data used in theclassifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetationindices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of oneclassifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. Inother words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specieamong others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maizecrop. 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 differentseasons, irrigated or not, have a high temporal spectral variation, which cannot be associated with these knowncharacteristics.</abstract>
		<area>SRE</area>
		<type>Agricultura e pecuária</type>
		<language>pt</language>
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