@InProceedings{OliveiraValMedOliKam:2022:FeClOp,
               author = "Oliveira, Andr{\'e}a de Lima and Val{\'e}rio, Aline de Matos and 
                         Medeiros, Thais Andrade Galv{\~a}o de and Oliveira, Nat{\'a}lia 
                         Rudorff and Kampel, Milton",
          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)}",
                title = "Feasibility of classifying optical water types from smartphones 
                         cameras",
                 year = "2022",
         organization = "Congreso Latinoamericano de Ciencias del Mar, 19.",
             abstract = "Antecedentes y justificaci{\'o}n: Orbital sensors have been used 
                         for ocean colour research and monitoring for decades with 
                         successful retrieval of chlorophyll-a concentrations, suspended 
                         matter and other biogeochemical and bio-optical properties. 
                         Recently, in situ sampling of remote sensing radiance has also 
                         been stimulated using smartphone ocean colour apps1. Planteamiento 
                         del problema y objetivos: The objective of this study was to 
                         verify the feasibility of using smartphone cameras to classify 
                         common water types in Brazil. Materiales y M{\'e}todos: We used 
                         74 in situ remote sensing reflectance spectra collected using 
                         hyperspectral radiometers in various water types (i.e., in the 
                         southeast coast, Abrolhos coral reef and Amazon River) to simulate 
                         the multispectral bands of smartphones (SSB)2 and apply an Optical 
                         Water Type (OWT) classification3. We also used simulated MODIS 
                         bands to compare the classification results. The K-means 
                         classification was applied to the hyperspectral data and simulated 
                         bands to distinguish the OWTs. Also, the Apparent Visible 
                         Wavelength (AVW) index was used to analyse the bands which 
                         contributed more to the classification. Resultados y 
                         Discusi{\'o}n: The results indicated that 4 classes were obtained 
                         and agreed in more than 95% for all sets of data (hyperspectral, 
                         MODIS and SSB). The AVW index revealed a higher contribution of 
                         the blue band to the first-class, red band to the second class and 
                         green band for the last two classes, which is related to the 
                         concentration of different biogeochemical constituents in the 
                         waters. Conclusiones: This study provides a wider perspective of 
                         how the smartphone cameras can be applied to water classification, 
                         which can be extended to ocean colour alteration track, in a 
                         citizen science approach.",
  conference-location = "Panam{\'a}",
      conference-year = "19-23 Sept. 2022",
             language = "en",
        urlaccessdate = "20 maio 2024"
}