@Article{Augusto-SilvaOgBaCaJoFoSt:2014:AnMERe,
               author = "Augusto-Silva, P{\'e}tala Bianchi and Ogashawara, Igor and 
                         Barbosa, Cl{\'a}udio Clemente Faria and Carvalho, Lino Augusto 
                         Sander de and Jorge, Daniel Schaffer Ferreira and Fornari, Celso 
                         Israel and Stech, Jos{\'e} Luiz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Analysis of MERIS Reflectance Algorithms for Estimating 
                         Chlorophyll-a Concentration in a Brazilian Reservoir",
              journal = "Remote Sensing",
                 year = "2014",
               volume = "6",
               number = "12",
                pages = "11689--11707",
             abstract = "Chlorophyll-a (chl-a) is a central water quality parameter that 
                         has been estimated through remote sensing bio-optical models. This 
                         work evaluated the performance of three well established 
                         reflectance based bio-optical algorithms to retrieve chl-a from in 
                         situ hyperspectral remote sensing reflectance datasets collected 
                         during three field campaigns in the Funil reservoir (Rio de 
                         Janeiro, Brazil). A Monte Carlo simulation was applied for all the 
                         algorithms to achieve the best calibration. The Normalized 
                         Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). 
                         The in situ hyperspectral dataset was used to simulate the Ocean 
                         Land Color Instrument (OLCI) spectral bands by applying its 
                         spectral response function. Therefore, we evaluated its 
                         applicability to monitor water quality in tropical turbid inland 
                         waters using algorithms developed for MEdium Resolution Imaging 
                         Spectrometer (MERIS) data. The application of OLCI simulated 
                         spectral bands to the algorithms generated results similar to the 
                         in situ hyperspectral: an error of 17.64% was found for NDCI. 
                         Thus, OLCI data will be suitable for inland water quality 
                         monitoring using MERIS reflectance based bio-optical algorithms.",
                  doi = "10.3390/rs61211689",
                  url = "http://dx.doi.org/10.3390/rs61211689",
                 issn = "2072-4292",
                label = "lattes: 2691497637313274 7 Augusto-SilvaOgBaCaJoFoSt:2014:AnMERe",
             language = "en",
           targetfile = "remotesensing-06-11689petala.pdf",
        urlaccessdate = "27 abr. 2024"
}