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@Article{MontanherNovBarRenSil:2014:EmMoEs,
               author = "Montanher, Ot{\'a}vio C. and Novo, Evlyn M{\'a}rcia Le{\~a}o de 
                         Moraes and Barbosa, Claudio Clemente Faria and Renn{\'o}, Camilo 
                         Daleles and Silva, Thiago S. F.",
          affiliation = "{Universidade Estadual de Maring{\'a}} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Universidade Estadual Paulista (UNESP)}",
                title = "Empirical models for estimating the suspended sediment 
                         concentration in Amazonian white water rivers using Landsat 5/TM",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2014",
               volume = "29",
                pages = "67--77",
                month = "June",
             keywords = "top of atmosphere reflectance, multiple regressions, geology of 
                         the Amazon, fluvial sediments, spectral bands, band ratios.",
             abstract = "Suspended sediment yield is a very important environmental 
                         indicator within Amazonian fluvial systems, especially for rivers 
                         dominated by inorganic particles, referred to as white water 
                         rivers. For vast portions of Amazonian rivers, suspended sediment 
                         concentration (SSC) is measured infrequently or not at all. 
                         However, remote sensing techniques have been used to estimate 
                         water quality parameters worldwide, from which data for suspended 
                         matter is the most successfully retrieved. This paper presents 
                         empirical models for SSC retrieval in Amazonian white water rivers 
                         using reflectance data derived from Landsat 5/TM. The models use 
                         multiple regression for both the entire dataset (global model, N = 
                         504) and for five segmented datasets (regional models) defined by 
                         general geological features of drainage basins. The models use 
                         VNIR bands, band ratios, and the SWIR band 5 as input. For the 
                         global model, the adjusted R2 is 0.76, while the adjusted R2 
                         values for regional models vary from 0.77 to 0.89, all significant 
                         (p-value < 0.0001). The regional models are subject to the 
                         leave-one-out cross validation technique, which presents robust 
                         results. The findings show that both the average error of 
                         estimation and the standard deviation increase as the SSC range 
                         increases. Regional models were more accurate when compared with 
                         the global model, suggesting changes in optical proprieties of 
                         water sampled at different sampling stations. Results confirm the 
                         potential for the estimation of SSC from Landsat/TM historical 
                         series data for the 1980s and 1990s, for which the in situ 
                         database is scarce. Such estimates supplement the SSC temporal 
                         series, providing a more comprehensive SSC temporal series which 
                         may show environmental dynamics yet unknown.",
                  doi = "10.1016/j.jag.2014.01.001",
                  url = "http://dx.doi.org/10.1016/j.jag.2014.01.001",
                 issn = "0303-2434",
                label = "self-archiving-INPE-MCTI-GOV-BR",
             language = "en",
                  url = "http://dx.doi.org/10.1016/j.jag.2014.01.001",
        urlaccessdate = "30 nov. 2020"
}


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