@Article{SmithPSREMGBBMFAK:2021:ChAlLa,
               author = "Smith, Brandon and Pahlevan, Nima and Schalles, John and Ruberg, 
                         Steve and Errera, Reagan and Ma, Ronghua and Giardino, Claudia and 
                         Bresciani, Mariano and Barbosa, Cl{\'a}udio Clemente Faria and 
                         Moore, Tim and Fern{\'a}ndez, Virginia and Alikas, Krista and 
                         Kangaro, Kersti",
          affiliation = "{NASA Goddard Space Flight Center} and {NASA Goddard Space Flight 
                         Center} and {Creighton University} and NOAA and NOAA and {Chinese 
                         Academy of Science} and {National Research Council of Italy} and 
                         {National Research Council of Italy} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Florida Atlantic University} and 
                         {University of the Republic} and {University of Tartu} and 
                         {University of Tartu}",
                title = "A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density 
                         Networks",
              journal = "Frontiers in Remote Sensing",
                 year = "2021",
               volume = "1",
                pages = "e623678",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "machine learning,, Landsat-8, Chlorophyll-a, Inland Waters, 
                         aquatic remote sensing.",
             abstract = "Retrieval of aquatic biogeochemical variables, such as the 
                         near-surface concentration of chlorophyll-a (Chla) in inland and 
                         coastal waters via remote observations, has long been regarded as 
                         a challenging task. This manuscript applies Mixture Density 
                         Networks (MDN) that use the visible spectral bands available by 
                         the Operational Land Imager (OLI) aboard Landsat-8 to estimate 
                         Chla. We utilize a database of co-located in situ radiometric and 
                         Chla measurements (N 4,354), referred to as Type A data, to train 
                         and test an MDN model (MDNA). This algorithms performance, having 
                         been proven for other satellite missions, is further evaluated 
                         against other widely used machine learning models (e.g., support 
                         vector machines), as well as other domain-specific solutions 
                         (OC3), and shown to offer significant advancements in the field. 
                         Our performance assessment using a held-out test data set suggests 
                         that a 49% (median) accuracy with near-zero bias can be achieved 
                         via the MDNA model, offering improvements of 20 to 100% in 
                         retrievals with respect to other models. The sensitivity of the 
                         MDNA model and benchmarking methods to uncertainties from 
                         atmospheric correction (AC) methods, is further quantified through 
                         a semi-global matchup dataset (N 3,337), referred to as Type B 
                         data. To tackle the increased uncertainties, alternative MDN 
                         models (MDNB) are developed through various features of the Type B 
                         data (e.g., Rayleigh-corrected reflectance spectra \ρs). 
                         Using heldout data, along with spatial and temporal analyses, we 
                         demonstrate that these alternative models show promise in 
                         enhancing the retrieval accuracy adversely influenced by the AC 
                         process. Results lend support for the adoption of MDNB models for 
                         regional and potentially global processing of OLI imagery, until a 
                         more robust AC method is developed. Index TermsChlorophyll-a, 
                         coastal water, inland water, Landsat-8, machine learning, ocean 
                         color, aquatic remote sensing.",
                  doi = "10.3389/frsen.2020.623678",
                  url = "http://dx.doi.org/10.3389/frsen.2020.623678",
                 issn = "2673-6187",
                label = "lattes: 1596449770636962 9 SmithPSREMGBBMFAK:2021:ChAlLa",
             language = "en",
           targetfile = "smith_chlorophyll.pdf",
        urlaccessdate = "04 maio 2024"
}