@InProceedings{MontanherNovoBarb:2013:MoEsCo,
               author = "Montanher, Ot{\'a}vio Cristiano and Novo, Evlyn Marcia Le{\~a}o 
                         de Moraes and Barbosa, Cl{\'a}udio Clemente Faria",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Modelos para estimativa da concentra{\c{c}}{\~a}o de sedimentos 
                         em suspens{\~a}o em rios amaz{\^o}nicos de {\'a}guas brancas 
                         via sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "5848--5855",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper presents empirical models for suspended sediment 
                         concentration (SSC) retrieval from Landsat 5 images in several 
                         Amazon Rivers. In view of great gaps in the time series of in situ 
                         SSC database in Amazon Rivers, estimates based on historical 
                         orbital data may be an option to open new research possibilities. 
                         The models are based on a database composed of 504 in situ samples 
                         and near-simultaneously Landsat images. Two approaches are tested: 
                         i) using the entire database and ii) regionalizing the data 
                         according to environmental features of the watersheds they belong. 
                         The results show that the use of the whole database does not 
                         provide accurate SSC estimates. The regional modeling provides 
                         better estimates by fragmenting the data into five clusters. All 
                         these models display p-values \≈ 1*10-6 , Rē values ranging 
                         from 0,83 to 0,91. The cross validation LOOCV and relative error 
                         values also showed their robustness. The models are very accurate, 
                         mainly for low SSC levels, between 0 to 200 mg/l. As the 
                         concentration increases, the absolute error increases too, but 
                         relative errors remain low (up to 7%).",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "405",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GD6K",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GD6K",
           targetfile = "p0405.pdf",
                 type = "Hidrologia",
        urlaccessdate = "16 jun. 2024"
}