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@Article{OgashawaraCuSoAuAlSt:2014:InCoEn,
               author = "Ogashawara, Igor and Curtarelli, Marcelo Pedroso and Souza, Arley 
                         F. and Augusto-Silva, P{\'e}tala Bianchi and Alc{\^a}ntara, 
                         Enner H. and Stech, Jos{\'e} Luiz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {ETEP Faculdades} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {State 
                         University of S{\~a}o Paulo} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Interactive correlation environment (ICE)-A statistical web tool 
                         for data collinearity analysis",
              journal = "Remote Sensing",
                 year = "2014",
               volume = "6",
               number = "4",
                pages = "3059--3074",
                month = "Apr.",
             keywords = "2D correlation, Band ratios, Bio-optical, Hyperspectral sensors, 
                         Interactive correlation, Open access platforms, Root mean square 
                         errors, Web tools, Biogeochemistry, Chlorophyll, Correlation 
                         methods, Mean square error, Optical correlation, Reservoirs 
                         (water), Tools, Ice.",
             abstract = "Web tools for statistical investigation with an interactive and 
                         friendly interface enable users without programming knowledge to 
                         conduct their analyses. We develop an Interactive Correlation 
                         Environment (ICE) in an open access platform to perform spectral 
                         collinearity analysis for biogeochemical activity retrieval. We 
                         evaluate its performance on different browsers and applied it to 
                         retrieve chlorophyll-a (chl-a) concentration in a tropical 
                         reservoir. The use of ICE to retrieve water chl-a concentration 
                         got a Root Mean Square Error (RMSE) lower than 7% for seasonal 
                         datasets, enhancing ICE's ability to adapt it within season. An 
                         RMSE of 17% was found for the mixed dataset with a large range of 
                         chl-a concentrations. We conclude that the use of ICE is 
                         recommended, due to its quick response, easily manipulation, high 
                         accuracy, and empirical adaptation to seasonal variability. Its 
                         use is enhanced by the development of hyperspectral sensors, which 
                         allow the identification of several biogeochemical components, 
                         such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf 
                         nitrogen, and leaf chl-a concentration.",
                  doi = "10.3390/rs6043059",
                  url = "http://dx.doi.org/10.3390/rs6043059",
                 issn = "2072-4292",
                label = "scopus 2014-05 CurtarelliOgStAuSoAl:2014:InCoEn",
             language = "en",
           targetfile = "remotesensing-06-03059.pdf",
                  url = "http://www.mdpi.com/2072-4292/6/4/3059",
        urlaccessdate = "29 nov. 2020"
}


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