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@Article{JorgeBaCaAfLoNo:2017:SNSiRa,
               author = "Jorge, Daniel Schaffer Ferreira and Barbosa, Cl{\'a}udio Clemente 
                         Faria and Carvalho, Lino Augusto Sander de and Affonso, Adriana 
                         Gomes and Lobo, Felipe de Lucia and Novo, Evlyn M{\'a}rcia 
                         Le{\~a}o de Moraes",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "SNR (signal-to-noise ratio) impact on water constituent retrieval 
                         from simulated images of optically complex Amazon lakes",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "7",
                pages = "Article number 644",
                month = "July",
             keywords = "signal-to-noise ratio, Remote Sensing Reflectance, bio-optical 
                         algorithms, inland waters.",
             abstract = "Uncertainties in the estimates of water constituents are among the 
                         main issues concerning the orbital remote sensing of inland 
                         waters. Those uncertainties result from sensor design, atmosphere 
                         correction, model equations, and in situ conditions (cloud cover, 
                         lake size/shape, and adjacency effects). In the Amazon floodplain 
                         lakes, such uncertainties are amplified due to their seasonal 
                         dynamic. Therefore, it is imperative to understand the suitability 
                         of a sensor to cope with them and assess their impact on the 
                         algorithms for the retrieval of constituents. The objective of 
                         this paper is to assess the impact of the SNR on the Chl-a and TSS 
                         algorithms in four lakes located at Mamirau{\'a} Sustainable 
                         Development Reserve (Amazonia, Brazil). Two data sets were 
                         simulated (noisy and noiseless spectra) based on in situ 
                         measurements and on sensor design (MSI/Sentinel-2, 
                         OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using 
                         three and four algorithms for TSS and Chl-a, respectively. The 
                         results showed that the impact of the SNR on each algorithm 
                         displayed similar patterns for both constituents. For additive and 
                         single band algorithms, the error amplitude is constant for the 
                         entire concentration range. However, for multiplicative 
                         algorithms, the error changes according to the model equation and 
                         the Rrs magnitude. Lastly, for the exponential algorithm, the 
                         retrieval amplitude is higher for a low concentration. The OLCI 
                         sensor has the best retrieval performance (error of up to 2 g/L 
                         for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive 
                         and single band algorithms for TSS and Chl-a are low (up to 5 mg/L 
                         and 1 g/L, respectively); but for the multiplicative algorithm, 
                         the errors were above 10 g/L. The OLI simulation resulted in 
                         errors below 3 mg/L for TSS. However, the number and position of 
                         OLI bands restrict Chl-a retrieval. Sensor and algorithm selection 
                         need a comprehensive analysis of key factors such as sensor 
                         design, in situ conditions, water brightness (Rrs), and model 
                         equations before being applied for inland water studies.",
                  doi = "10.3390/rs9070644",
                  url = "http://dx.doi.org/10.3390/rs9070644",
                 issn = "2072-4292",
             language = "en",
           targetfile = "jorge_snr.pdf",
        urlaccessdate = "29 nov. 2020"
}


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