author = "Watanabe, Fernanda Sayuri Yoshino and Alc{\^a}ntara, Enner and 
                         Stech, Jos{\'e} Luiz",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade 
                         Estadual Paulista (UNESP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "High performance of chlorophyll-a prediction algorithms based on 
                         simulated OLCI Sentinel-3A bands in cyanobacteria-dominated inland 
              journal = "Advances in Space Research",
                 year = "2018",
               volume = "62",
               number = "2",
                pages = "265--273",
                month = "July",
             keywords = "Harmful algal bloom, Case-2 waters, Remote sensing, Water 
             abstract = "In this research, we have investigated whether the chlorophyll-a 
                         (chl a) retrieval algorithms based on OLCI Sentinel-3A bands are 
                         suitable for cyanobacteria-dominated waters. Phytoplankton 
                         assemblages model optical properties of the water, influencing the 
                         performance of bio-optical algorithms. Understanding these 
                         processes is important to improve the prediction of photoactive 
                         pigments in order to use them as a proxy for trophic state and 
                         harmful algal bloom. So that, both empirical and semi-analytical 
                         approaches designed for different inland waters were tested. In 
                         addition, empirical models were tuned based on dataset collected 
                         in situ. The study was conducted in the Funil hydroelectric 
                         reservoir, where chl a ranged from 2.33 to 208.68 mg m3 in May 
                         2012 (austral fall) and 4.37 to 306.03 mg m3 in October 2012 
                         (austral spring). OLCI Sentinel-3A bands were tested in existing 
                         algorithms developed for other sensors and new band combinations 
                         were compared to analyze the errors produced. Normalized 
                         Difference Chlorophyll Index (NDCI) exhibited the best 
                         performance, with a Normalized Root Mean Square Error (NRMSE) of 
                         9.30%. Result showed that wavelength at 665 nm is adequate to 
                         estimate chl a, although the maximum pigment absorption band is 
                         shifted due to phycocyanin fluorescence at approximately 650 nm. 
                         2018 COSPAR. Published by Elsevier Ltd. All rights reserved.",
                  doi = "10.1016/j.asr.2018.04.024",
                  url = "http://dx.doi.org/10.1016/j.asr.2018.04.024",
                 issn = "0273-1177 and 1879-1948",
             language = "en",
           targetfile = "watanabe_high.pdf",
        urlaccessdate = "11 maio 2021"