@InProceedings{MontibellerLuizSancSilv:2017:AnVaEs,
               author = "Montibeller, Bruno and Luiz, Alfredo Jos{\'e} Barreto and 
                         Sanches, Ieda Del Arco and Silveira, Hilton Lu{\'{\i}}s Ferraz 
                         da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise da variabilidade espectro-temporal 
                         intraespec{\'{\i}}fica do milho",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2011--2018",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             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.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59410",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLPQ2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLPQ2",
           targetfile = "59410.pdf",
                 type = "Agricultura e pecu{\'a}ria",
        urlaccessdate = "28 abr. 2024"
}