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@Article{OgashawaraMisMisCurSte:2013:PeReRe,
               author = "Ogashawara, Igor and Mishra, Deepak and Mishra, Sachidananda and 
                         Curtarelli, Marcelo Pedroso and Stech, Jose Luiz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Univ 
                         Georgia, Dept Geog, Athens, GA 30602 USA and Dow Agrosci, 
                         Indianapolis, IN 46268 USA and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "A Performance Review of Reflectance Based Algorithms for 
                         Predicting Phycocyanin Concentrations in Inland Waters",
              journal = "Remote Sensing",
                 year = "2013",
               volume = "5",
               number = "10",
                pages = "4774--4798",
                month = "Oct.",
             keywords = "cyanobacteria, phycocyanin, chlorophyll-a, band ratio, remote 
                         sensing reflectance, hyperspectral sensors, chlorophyll-a 
                         concentration, turbid productive waters, cyanobacterial blooms, 
                         remote, pigments.",
             abstract = "We evaluated the accuracy and sensitivity of six previously 
                         published reflectance based algorithms to retrieve Phycocyanin 
                         (PC) concentration in inland waters. We used field radiometric and 
                         pigment data obtained from two study sites located in the United 
                         States and Brazil. All the algorithms targeted the PC absorption 
                         feature observed in the water reflectance spectra between 600 and 
                         625 nm. We evaluated the influence of chlorophyll-a (chl-a) 
                         absorption on the performance of these algorithms in two 
                         contrasting environments with very low and very high cyanobacteria 
                         content. All algorithms performed well in low to moderate PC 
                         concentrations and showed signs of saturation or decreased 
                         sensitivity for high PC concentration with a nonlinear trend. MM09 
                         was found to be the most accurate algorithm overall with a RMSE of 
                         15.675%. We also evaluated the use of these algorithms with the 
                         simulated spectral bands of two hyperspectral space borne sensors 
                         including Hyperion and Compact High-Resolution Imaging 
                         Spectrometer (CHRIS) and a hyperspectral air borne sensor, 
                         Hyperspectral Infrared Imager (HyspIRI). Results showed that the 
                         sensitivity for chl-a of PC retrieval algorithms for Hyperion 
                         simulated data were less noticable than using the spectral bands 
                         of CHRIS; HyspIRI results show that SC00 could be used for this 
                         sensor with low chl-a influence. This review of reflectance based 
                         algorithms can be used to select the optimal approach in studies 
                         involving cyanobacteria monitoring through optical remote sensing 
                         techniques.",
                  doi = "10.3390/rs5104774",
                  url = "http://dx.doi.org/10.3390/rs5104774",
                 issn = "2072-4292",
                label = "lattes: 2691497637313274 5 OgashawaraMisMisCurSte:2013:PeReRe",
             language = "en",
        urlaccessdate = "24 jan. 2021"
}


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