author = "Ogashawara, Igor and Alc{\^a}ntara, Enner H. and Curtarelli, 
                         Marcelo Pedroso and Adami, Marcos and Nascimento, Renata F. F. and 
                         Souza, Arley F. and Stech, Jose Luiz and Kampel, Milton",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Cartography 
                         Engineering Department, State University of S{\~a}o Paulo, Rua 
                         Roberto Simonsen 305, Presidente Prudente-SP 19060-900, Brazil and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and 
                         Geopixel-Solu{\c{c}}{\~o}es em Geotecnologias, Rua Maestro 
                         Egydio Pinto 190, S{\~a}o Jos{\'e} dos Campos-SP 12245-902, 
                         Brazil and ETEP Faculdades, Avenida Bar{\~a}o Rio Branco 882, 
                         S{\~a}o Jos{\'e} dos Campos-SP 12242-800, Brazil and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Performance analysis of MODIS 500-m spatial resolution products 
                         for estimating chlorophyll-a concentrations in oligo- to 
                         meso-trophic waters case study: Itumbiara reservoir, Brazil",
              journal = "Remote Sensing",
                 year = "2014",
               volume = "6",
               number = "2",
                pages = "1634--1653",
             keywords = "Bio-optic modeling, Chlorophyll-a, MODIS, Time-series.",
             abstract = "Monitoring chlorophyll-a (chl-a) concentrations is important for 
                         the management of water quality, because it is a good indicator of 
                         the eutrophication level in an aquatic system. Thus, our main 
                         purpose was to develop an alternative technique to monitor chl-a 
                         in time and space through remote sensing techniques. However, one 
                         of the limitations of remote sensing is the resolution. To achieve 
                         a high temporal resolution and medium space resolution, we used 
                         the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m 
                         reflectance product, MOD09GA, and limnological parameters from the 
                         Itumbiara Reservoir. With these data, an empirical (O14a) and 
                         semi-empirical (O14b) algorithm were developed. Algorithms were 
                         cross-calibrated and validated using three datasets: one for each 
                         campaign and a third consisting of a combination of the two 
                         individual campaigns. Algorithm O14a produced the best validation 
                         with a root mean square error (RMSE) of 30.4%, whereas O14b 
                         produced an RMSE of 32.41% using the mixed dataset calibration. 
                         O14a was applied to MOD09GA to build a time series for the 
                         reservoir for the year of 2009. The time-series analysis revealed 
                         that there were occurrences of algal blooms in the summer that 
                         were likely related to the additional input of nutrients caused by 
                         rainfall runoff. During the winter, however, the few observed 
                         algal blooms events were related to periods of atmospheric 
                         meteorological variations that represented an enhanced external 
                         influence on the processes of mixing and stratification of the 
                         water column. Finally, the use of remote sensing techniques can be 
                         an important tool for policy makers, environmental managers and 
                         the scientific community with which to monitor water quality.",
                  doi = "10.3390/rs6021634",
                  url = "http://dx.doi.org/10.3390/rs6021634",
                 issn = "2072-4292",
                label = "scopus 2014-05 OgashawaraACANSSK:2014:ItReBr",
             language = "en",
           targetfile = "remotesensing-06-01634.pdf",
        urlaccessdate = "04 dez. 2020"