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@InProceedings{MoreiraTeixGalv:2015:AnQuCo,
               author = "Moreira, Luis Clenio J{\'a}rio and Teixeira, Adunias dos Santos 
                         and Galv{\~a}o, L{\^e}nio Soares",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise quantitativa da concentra{\c{c}}{\~a}o de sais nos 
                         solos a partir de dados de espectroscopia de reflect{\^a}ncia",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3919--3926",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The aim of this study was to evaluate the possibility of using 
                         reflectance spectroscopy to quantify the salt concentration of the 
                         soil enabling the use of hyperspectral imagery for mapping large 
                         degraded areas. To develop statistical models 93 soil samples, and 
                         to calibrate 2/3 and 1/3 were used to validate. In the two sample 
                         subsets spectral measurements were made in the laboratory using 
                         the spectroradiometer FieldSpec Pro under controlled conditions 
                         and measurements of electrical conductivity (EC) were performed. 
                         Three statistical models were used to analyze the reflectance vs 
                         EC: linear regression, normalized salinity index (NDSI) and 
                         partial least squares regression (PLSR). The linear model was 
                         developed to better results with the band positioned at 1945 nm 
                         showing significant predictive power (R2 = 0.50, RMSE = 0.987 and 
                         RPD = 1.47). Still, it was lower than the model developed from the 
                         NDSI (using 1875 and 1935 nm with R2 = 0.836; RMSE = 0.54; RPD = 
                         2.44). Two PLSR models were constructed: one using all the 
                         spectral information (PLSR1) and other bands without atmospheric 
                         interference (PLSR2). The PLSR1 showed better results (R2 = 0.883, 
                         RMSE = 0.44 and RPD = 2.90) compared to the other models developed 
                         in this work suggest that the greater the number of spectral 
                         information used in the modeling, the greater the ability to 
                         predict. However, the optimization of the number of variables to 
                         compose the predictive models may be made, enabling better results 
                         with the least number of possible input variables.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "780",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4C8L",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4C8L",
           targetfile = "p0780.pdf",
                 type = "Sensoriamento remoto hiperespectral",
        urlaccessdate = "02 dez. 2020"
}


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