@InProceedings{JorgeAmorBarb:2015:EfEsFo,
               author = "Jorge, Daniel Schaffer Ferreira and Amore, Diogo de Jesus and 
                         Barbosa, Claudio Clemente Faria",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Efficiency estimation of four different atmospheric correction 
                         algorithms in a sediment-loaded tropic lake for Landsat 8 OLI 
                         sensor",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4428--4435",
         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 = "Atmospheric correction algorithms allow the reduction of 
                         atmospheric components influence in the acquisition of earths 
                         surface reflectance properties. Quantifying this reduction is 
                         critical for the remote sensing science. Different regions have 
                         different natural or man-made composition and respond differently 
                         for each algorithm. Highly turbid inland waters are significantly 
                         sensitive to atmospheric correction algorithms, and these waters 
                         must be evaluated with care. This paper investigated the impact of 
                         turbidity levels in a sediment-laden tropical lake through the 
                         correlation analysis between image and ground-based Remote Sensing 
                         reflectance (Rrs) for the Landsat 8 OLI sensor. Four atmospheric 
                         algorithms were tested for Rrs estimation: DOS (Dark Object 
                         Substraction), FLAASH (Fast Line-of-sight Atmospheric Analysis of 
                         Spectral Hypercubes), 6S (Second Simulation of the Satellite 
                         Signal in the Solar Spectrum) and QUAC (QUick Atmospheric 
                         Correction). A regression linear model was applied to the data, 
                         and results showed two distinguishable features within the 
                         samples. The first feature set presents a near-one higher-angle 
                         slope value with R2 values ranging from 49-64%, and a near-zero 
                         lower-angle slope with R2 ranging from 49-71%. Despite the small 
                         data set used in this work, it is reasonable to assume the results 
                         demonstrate that, during the atmospheric correction process, the 
                         Rrs correlation undergoes a slope direction change. This is most 
                         likely due to the influence of higher turbidity levels.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "870",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CPH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4CPH",
           targetfile = "p0870.pdf",
                 type = "Sensoriamento remoto de {\'a}guas interiores",
        urlaccessdate = "27 abr. 2024"
}