@Article{MacielNoCaBaFlLo:2019:MuAp,
               author = "Maciel, Daniel Andrade and Novo, Evlyn M{\'a}rcia Le{\~a}o de 
                         Moraes and Carvalho, Lino Augusto Sander de and Barbosa, 
                         Cl{\'a}udio Clemente Faria and Flores J{\'u}nior, Rog{\'e}rio 
                         and Lobo, Felipe de Lucia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         do Rio de Janeiro (UFRJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Retrieving total and inorganic suspended sediments in Amazon 
                         floodplain lakes: a multisensor approach",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "15",
                pages = "e1744",
                month = "Aug.",
             keywords = "suspended sediments, Amazon Floodplains, Optically Complex Waters, 
                         Monte Carlo Simulation, inorganic sediments.",
             abstract = "Remote sensing imagery are fundamental to increasing the knowledge 
                         about sediment dynamics in the middle-lower Amazon floodplains. 
                         Moreover, they can help to understand both how climate change and 
                         how land use and land cover changes impact the sediment exchange 
                         between the Amazon River and floodplain lakes in this important 
                         and complex ecosystem. This study investigates the suitability of 
                         Landsat-8 and Sentinel-2 spectral characteristics in retrieving 
                         total (TSS) and inorganic (TSI) suspended sediments on a set of 
                         Amazon floodplain lakes in the middle-lower Amazon basin using in 
                         situ Remote Sensing Reflectance (Rrs) measurements to simulate 
                         Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI 
                         (Multispectral Instrument) bands and to calibrate/validate several 
                         TSS and TSI empirical algorithms. The calibration was based on the 
                         Monte Carlo Simulation carried out for the following datasets: (1) 
                         All-Dataset, consisting of all the data acquired during four field 
                         campaigns at five lakes spread over the lower Amazon floodplain (n 
                         = 94); (2) Campaign-Dataset including samples acquired in a 
                         specific hydrograph phase (season) in all lakes. As sample size 
                         varied from one season to the other, n varied from 18 to 31; (3) 
                         Lake-Dataset including samples acquired in all seasons at a given 
                         lake with n also varying from 17 to 67 for each lake. The 
                         calibrated models were, then, applied to OLI and MSI scenes 
                         acquired in August 2017. The performance of three atmospheric 
                         correction algorithms was also assessed for both OLI (6S, ACOLITE, 
                         and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of 
                         glint correction on atmosphere-corrected image performance was 
                         assessed against in situ glint-corrected Rrs measurements. After 
                         glint correction, the L8SR and 6S atmospheric correction performed 
                         better with the OLI and MSI sensors, respectively (Mean Absolute 
                         Percentage Error (MAPE) = 16.68% and 14.38%) considering the 
                         entire set of bands. However, for a given single band, different 
                         methods have different performances. The validated TSI and TSS 
                         satellite estimates showed that both in situ TSI and TSS 
                         algorithms provided reliable estimates, having the best results 
                         for the green OLI band (561 nm) and MSI red-edge band (705 nm) 
                         (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI 
                         models provided similar errors, which support the use of both 
                         sensors as a virtual constellation for the TSS and TSI estimate 
                         over an Amazon floodplain. These results demonstrate the 
                         applicability of the calibration/validation techniques developed 
                         for the empirical modeling of suspended sediments in lower Amazon 
                         floodplain lakes using medium-resolution sensors.",
                  doi = "10.3390/rs11151744",
                  url = "http://dx.doi.org/10.3390/rs11151744",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-11-01744.pdf",
        urlaccessdate = "21 maio 2024"
}