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@InProceedings{FloresJ˙niorMCCLCNB:2019:AsSaAl,
               author = "Flores J{\'u}nior, Rog{\'e}rio and Maciel, Daniel Andrade and 
                         Cairo, Carolline Tressmann and Carlos, Felipe Menino and Lobo, 
                         Felipe de Lucia and Carvalho, Lino Augusto Sander de and Novo, 
                         Evlyn M{\'a}rcia Le{\~a}o de Moraes and Barbosa, Cl{\'a}udio 
                         Clemente Faria",
          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 {Universidade Federal do Rio de Janeiro (UFRJ)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Assessment of satellite algorithms for deriving chlorophyll-a from 
                         turbid waters of Amazon floodplain lakes",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1938--1941",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Water quality, Chlorophyll-a, Empirical algorithms, Remote 
                         sensing, Landsat, Sentinel.",
             abstract = "The Amazon floodplain represents one of the most important 
                         terrestrial ecosystems being a highly complex and dynamic 
                         environment, with a key role in the global carbon cycle. 
                         Therefore, the monitoring and management of their aquatic systems 
                         is vital to increase the knowledge on the biogeochemistry 
                         involving water components. Optically Active Components (OACs) as 
                         chlorophyll-a (chl-a) can be a proxy to environmental parameters 
                         such as water trophic status and primary productivity. Standard 
                         methods to determine chl-a are based on in situ measurements being 
                         expensive and time consuming, alternatively, remote sensing can be 
                         a viable option through the calibration of chl-a algorithms. 
                         Therefore, this work aims the assessment of empirical algorithms 
                         for chl-a retrieval in Amazon lakes with turbid waters using 
                         Remote Sensing reflectance (Rrs) from in situ data gathered in 
                         four campaigns between 2015 and 2017. In situ Rrs was then used to 
                         simulate Landsat 8/OLI and Sentinel 2/MSI images which were 
                         calibrated and validated by Monte Carlo simulation. The best 
                         algorithms were validated using images acquired almost 
                         concurrently to in situ data acquisition for both sensors. 
                         Preliminary results pointed out the ability to estimate chl-a with 
                         errors smaller than 30% for MAPE for simulated data.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3UA4L38",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3UA4L38",
           targetfile = "97890.pdf",
                 type = "Sensoriamento remoto de {\'a}guas interiores",
        urlaccessdate = "24 jan. 2021"
}


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