@InProceedings{RechHessSouzUda:2023:EfAtCo,
               author = "Rech, Bruno and Hess, Jos{\'e} Henrique and Souza J{\'u}nior, 
                         Silvio Jo{\~a}o de and Uda, Patr{\'{\i}}cia Kazue",
          affiliation = "{Universidade Federal de Santa Catarina (UFSC)} and {Universidade 
                         Federal de Santa Catarina (UFSC)} and {Universidade Federal de 
                         Santa Catarina (UFSC)} and {Universidade Federal de Santa Catarina 
                         (UFSC)}",
                title = "Effects of atmospheric correction on NDVI retrieved from 
                         Sentinel-2 imagery over different land cover classes",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155758",
         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 = "vegetation index, Sentinel-2, Google Earth Engine, satellite 
                         images, Lagoa da Concei{\c{c}}{\~a}o.",
             abstract = "Amongst several existing vegetation indices, NDVI Normalized 
                         Difference Vegetation Index is the most famous one. Its 
                         applications range from crop monitoring to surface emissivity 
                         estimations. Although NDVI provides several benefits on 
                         highlighting vegetation features, it is also affected by image 
                         characteristics and atmospheric composition. Due to the importance 
                         of normalized vegetation indices to various fields of study, the 
                         present research seeks to detect and to evaluate the effects of 
                         atmospheric correction on NDVI values retrieved from Sentinel-2 
                         images over distinct land cover classes. Scenes with 
                         top-of-atmosphere and bottom-ofatmosphere reflectance were 
                         selected, and a 6S atmospheric correction algorithm was applied to 
                         generate a third dataset (116 images each). NDVI was calculated 
                         and the mean of each scene was evaluated to seven land cover 
                         classes, including vegetation, urbanization and water-covered 
                         areas. The results showed that atmospheric correction increases 
                         NDVI in vegetation areas, while dunes, urban and watercovered 
                         surfaces presented the largest errors.",
  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/493UBJP",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/493UBJP",
           targetfile = "155758.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "05 maio 2024"
}