@Article{SilvaNoLoBaCaNoRo:2021:MaLeAp,
               author = "Silva, Edson Filisbino Freire da and Novo, Evlyn M{\'a}rcia 
                         Le{\~a}o de Moraes and Lobo, Felipe de Lucia and Barbosa, 
                         Cl{\'a}udio Clemente Faria and Cairo, Carolline Tressmann and 
                         Noernberg, Maur{\'{\i}}cio Almeida and Rotta, Luiz Henrique da 
                         Silva",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Pelotas (UFPel)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Paran{\'a} (UFPR)} and {Universidade 
                         Estadual Paulista (UNESP)}",
                title = "A machine learning approach for monitoring Brazilian optical water 
                         types using Sentinel-2 MSI",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2021",
               volume = "23",
                pages = "e100577",
                month = "Aug.",
             keywords = "Classification, Machine learning, Novelty detection, Optical water 
                         type.",
             abstract = "Optical Water Type (OWT) is a useful parameter for assessing water 
                         quality changes related to different turbidity levels, trophic 
                         state and colored dissolved organic matter (CDOM) while also 
                         helpful for tuning chlorophyll-a algorithms. For this reason, 
                         interest in the satellite remote sensing of OWTs has recently 
                         increased in recent years. This study develops a machine learning 
                         method for monitoring Brazilian OWTs using the Sentinel-2 MSI, 
                         which can detect OWTs already assessed by field measurements and 
                         recognize new OWTs. The already assessed OWTs used for calibrating 
                         the machine learning algorithm are clear, moderate turbid, 
                         eutrophic turbid, eutrophic clear, hypereutrophic, CDOM richest, 
                         turbid, and very turbid waters. The classification method consists 
                         of two Support Vector Machines for classifying the known OWTs, 
                         while a novelty detection method based on sigmoid functions is 
                         used for assessing new OWTs. Results show the classification based 
                         on Sentinel-2 MSI bands simulated using field radiometric data is 
                         accurate (accuracy = 0.94). However, when radiometric errors are 
                         simulated, the accuracy significantly decreases to 0.75, 0.56, 
                         0.45, and 0.37 as the mean absolute percent error increases to 
                         10%, 20%, 30%, and 40%, respectively. Considering the errors 
                         retrieved when comparing the field and satellite measurements, the 
                         expected accuracy of Sentinel-2 MSI images is 0.78. In the 
                         satellite images, the novelty detection distinguishes new OWTs 
                         originated from the mixture among the known OWTs and a new OWT 
                         that was not part of the training database (clear blue waters). 
                         Two examples of time series in the Funil reservoir and the Curuai 
                         lake are used to show the applicability of monitoring OWTs. In the 
                         Funil reservoir, OWTs could indicate eutrophication and turbid 
                         changes caused by river inflow and sediment sinking. In the Curuai 
                         lake, OWTs could indicate areas susceptible to algae bloom and 
                         turbidity increases related to river inflow and particle 
                         resuspension. In the future, the proposed algorithm could be used 
                         for large-scale assessment of water quality degradation and 
                         supports rapid mitigation and recovery responses. For improving 
                         the classification accuracy, adjacency correction and more robust 
                         glint removal methods should be developed.",
                  doi = "10.1016/j.rsase.2021.100577",
                  url = "http://dx.doi.org/10.1016/j.rsase.2021.100577",
                 issn = "2352-9385",
             language = "en",
           targetfile = "silva_machine_2021.pdf",
        urlaccessdate = "04 maio 2024"
}