@Article{MacielBNCMFJCC:2020:MaDiAt,
               author = "Maciel, Daniel Andrade and Barbosa, Cl{\'a}udio Clemente Faria 
                         and Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes and Cherukuru, 
                         Nagur and Martins, Vitor Souza and Flores J{\'u}nior, 
                         Rog{\'e}rio and Jorge, Daniel Schaffer and Carvalho, Lino Augusto 
                         Sander de and Carlos, Felipe Menino",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {CSIRO Oceans and Atmosphere} and 
                         {Michigan State University} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Univ. Littoral Cot{\^e} d’Opale} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapping of diffuse attenuation coefficient in optically complex 
                         waters of amazon floodplain lakes",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2020",
               volume = "170",
                pages = "72--87",
                month = "Dec.",
             keywords = "Kd, Turbid waters, Diffuse attenuation coefficient, Sentinel-2, 
                         Complex waters, Atmospheric correction.",
             abstract = "The modeling of underwater light field is essential for the 
                         understanding of biogeochemical processes, such as photosynthesis, 
                         carbon fluxes, and sediment transports in inland waters. 
                         Water-column light attenuation can be quantified by the diffuse 
                         attenuation coefficient of the downwelling irradiance (Kd) using 
                         semi-analytical algorithms (SAA). However, the accuracy of these 
                         algorithms is currently limited in highly turbid environments, 
                         such as Amazon Floodplains, due to the SAA parametrization steps. 
                         In this study, we assessed an SAA approach for Kd retrieval using 
                         a sizeable (n = 239) and diverse dataset (e.g., Kd (490) ranging 
                         from almost 0 to up to 30 m\−1 with mean values of 5.75 ± 
                         3.94 m\−1) in Amazon freshwater ecosystem. The main 
                         framework of this study consists of i) re-parametrization of a 
                         quasi-analytical algorithm using regional in-situ inherent optical 
                         properties (IOPs) and ii) application and validation of SAA for Kd 
                         retrieval using in-situ and Sentinel-2/MSI (n = 49) derived from 
                         Remote Sensing Reflectance (Rrs). Overall, the performance of the 
                         calibrated SAA was satisfactory for both in-situ and satellite 
                         Rrs. The validation results with in-situ data achieved a Mean 
                         Absolute Percentage Error (MAPE) lower than 22%, Correlation 
                         Coefficient (R) > 0.80, Root Mean Square Error (RMSE) lower than 
                         1.7 m\−1 and bias between 0.73 and 1.34 for simulated 
                         visible bands of Sentinel-2/MSI (490, 560 and 660 nm) (VIS). The 
                         results using MSI imagery were similar to those of in-situ, with R 
                         > 0.9, MAPE < 20%, RMSE < 1.25 m\−1, and bias between 0.98 
                         and 1.10 for VIS bands, which illustrate the viability of this 
                         methodology for Kd mapping in Amazon Floodplain Lakes. Therefore, 
                         this study demonstrates a successful application of satellite 
                         remote sensing data for the spatialization of the Kd in the 
                         optically complex waters of Amazon Basin, which is essential for 
                         the ecological management of the Amazon Floodplain Lakes.",
                  doi = "10.1016/j.isprsjprs.2020.10.009",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2020.10.009",
                 issn = "0924-2716",
             language = "en",
           targetfile = "maciel_mapping.pdf",
        urlaccessdate = "24 abr. 2024"
}