@Article{FloresJúniorBMNMLCC:2022:HySeAl,
               author = "Flores J{\'u}nior, Rog{\'e}rio and Barbosa, Cl{\'a}udio 
                         Clemente Faria and Maciel, Daniel Andrade and Novo, Evlyn 
                         M{\'a}rcia Le{\~a}o de Moraes and Martins, Vitor Souza and Lobo, 
                         Felipe de Lucia 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 {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Mississippi State University} and 
                         {Universidade Federal de Pelotas (UFPel)} and {Universidade 
                         Federal Do Rio de Janeiro (UFRJ)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Hybrid Semi-Analytical Algorithm for Estimating Chlorophyll-A 
                         Concentration in Lower Amazon Floodplain Waters",
              journal = "Frontiers in Remote Sensing",
                 year = "2022",
               volume = "3",
                pages = "1--20",
             keywords = "Chlorophyll-a, water quality and clarity, Amazon Floodflain, 
                         inherent and apparent optical properties, , turbid water.",
             abstract = "The Amazon Basin is the largest on the planet, and its aquatic 
                         ecosystems affect and are affected by the Earths processes. 
                         Specifically, Amazon aquatic ecosystems have been subjected to 
                         severe anthropogenic impacts due to deforestation, mining, dam 
                         construction, and widespread agribusiness expansion. Therefore, 
                         the monitoring of these impacts has become crucial for 
                         conservation plans and environmental legislation enforcement. 
                         However, its continental dimensions, the high variability of 
                         Amazonian water mass constituents, and cloud cover frequency 
                         impose a challenge for developing accurate satellite algorithms 
                         for water quality retrieval such as chlorophyll-a concentration 
                         (Chl-a), which is a proxy for the trophic state. This study 
                         presents the first application of the hybrid semi-analytical 
                         algorithm (HSAA) for Chl-a retrieval using a Sentinel-3 OLCI 
                         sensor over five Amazonian floodplain lakes. Inherent and apparent 
                         optical properties (IOPs and AOPs), as well as limnological data, 
                         were collected at 94 sampling stations during four field campaigns 
                         along hydrological years spanning from 2015 to 2017 and used to 
                         parameterize the hybrid SAA to retrieve Chl-a in highly turbid 
                         Amazonian waters. We implemented a re-parametrizing approach, 
                         called the generalized stacked constraints model to the Amazonian 
                         waters (GSCMLAFW), and used it to decompose the total absorption 
                         \αt(\λ) into the absorption coefficients of detritus, 
                         CDOM, and phytoplankton (\αphy(\λ)). The estimated 
                         GSCMLAFW \αphy(\λ) achieved errors lower than 24% at 
                         the visible bands and 70% at NIR. The performance of HSAA-based 
                         Chl-a retrieval was validated with in situ measurements of Chl-a 
                         concentration, and then it was compared to literature Chl-a 
                         algorithms. The results showed a smaller mean absolute percentage 
                         error (MAPE) for HSAA Chl-a retrieval (36.93%) than empirical Rrs 
                         models (73.39%) using a 3-band algorithm, which confirms the 
                         better performance of the semi-analytical approach. Last, the 
                         calibrated HSAA model was used to estimate the Chl-a concentration 
                         in OLCI images acquired during 2017 and 2019 field campaigns, and 
                         the results demonstrated reasonable errors (MAPE = 57%) and 
                         indicated the potential of OLCI bands for Chl-a estimation. 
                         Therefore, the outcomes of this study support the advance of 
                         semianalytical models in highly turbid waters and highlight the 
                         importance of reparameterization with GSCM and the applicability 
                         of HSAA in Sentinel-3 OLCI data.",
                  doi = "10.3389/frsen.2022.834576",
                  url = "http://dx.doi.org/10.3389/frsen.2022.834576",
                 issn = "2673-6187",
                label = "lattes: 1596449770636962 2 FloresJ{\'u}niorBMNMLSC:2022:HySeAl",
             language = "en",
           targetfile = "frsen-03-834576.pdf",
        urlaccessdate = "25 jun. 2024"
}