@InProceedings{WanderleyPaMaBaNoDo:2023:EsAnTr,
               author = "Wanderley, Raianny Leite do Nascimento and Paulino, Rejane de 
                         Souza and Maciel, Daniel de Andrade and Barbosa, Cl{\'a}udio 
                         Clemente Faria and Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes and 
                         Domingues, Leonardo Moreno",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade de S{\~a}o Paulo (USP)}",
                title = "Estimation of annual trophic state index distributions of a 
                         tropical reservoir using Landsat imagery time series",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155818",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Water quality, Inland waters, Landsat-8, Modelling, Random 
                         Forest.",
             abstract = "The eutrophication of reservoirs significantly impacts human 
                         health and environmental security. However, in situ water quality 
                         monitoring can be expensive once it includes equipment and human 
                         resources. An effective proxy for water quality is the Trophic 
                         State Index (TSI) Chlorophyll-a (chl-a) based. Remote sensing 
                         techniques have helped the authorities and scientific community to 
                         map TSI worldwide. Then, this study aimed to develop a remote 
                         sensing-based TSI algorithm and estimate the TSI spatiotemporal 
                         distribution in a reservoir in Brazil. The chl-a concentration was 
                         used as a proxy to TSI and classified into three classes: 
                         OligoMeso, EutroSuper, and Hyper. The calibrated algorithm was 
                         applied to the Jaguari-Jacare{\'{\i}} reservoir to obtain TSI 
                         between 2013 and 2022. Classification results achieved an overall 
                         accuracy of 75% for a validation dataset. Although the general 
                         pattern of the TSI in the reservoir is majority OligoMeso, the 
                         results indicate two patterns established according to dry and wet 
                         seasons.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/48TS7QS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/48TS7QS",
           targetfile = "155818.pdf",
                 type = "Sensoriamento remoto de {\'a}guas interiores",
        urlaccessdate = "30 abr. 2024"
}