@InProceedings{AlvesFreiCampRoig:2017:UsSeRe,
               author = "Alves, Welber Ferreira and Freitas, Erica Yoshida de and Campos, 
                         Camila Aida and Roig, Henrique Llacer",
                title = "Uso de Sensoriamento Remoto para preenchimento de falhas em 
                         s{\'e}ries pluviom{\'e}tricas",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7812--7818",
         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 = "It is very important to have hydrology data, because the 
                         possibility to know about characteristics from a place and so to 
                         plan about the trends, even though because it is common history to 
                         repeat. But it is very expensive and difficult to have data from 
                         many places, and this fact can be serious in critical areas. This 
                         way, remote sensing can be a good solution to solve these 
                         problems. So this paper analyzes data from satellite TRMM, one of 
                         the main, and interpolation techniques IDW and Thiessen Polygons 
                         using data from Rain Gauges localized in Distrito Federal, 
                         Brazilians capital, in order to allow using data from remote 
                         sensing to fill blanks in annual data and averages data from 
                         stations points or areas without it. After comparatives it is 
                         possible to identify low differences between these data, principal 
                         comparing averages region annual, although the data from TRMM is 
                         lower than others when it compares point by point. So if the 
                         necessity is annual data or annual averages it is a good choice 
                         using data from remote sensing to areas without monitoring points, 
                         knowing its limitations, but to complete blanks in months or days 
                         it is possible the error is very high.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59802",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMGF8",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGF8",
           targetfile = "59802.pdf",
                 type = "Hidrologia",
        urlaccessdate = "27 abr. 2024"
}