@InProceedings{OliveiraEpip:2012:NoReMo,
               author = "Oliveira, Julio Cesar de and Epiphanio, Jos{\'e} Carlos Neves",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Noise reduction in modis NDVI time series data based on 
                         spatial-temporal analysis",
            booktitle = "Proceedings...",
                 year = "2012",
                pages = "2372 - 2375",
         organization = "International Geoscience and Remote Sensing Symposium, 32. 
                         (IGARSS).",
            publisher = "IEEE Geoscience and Remote Sensing Society",
             keywords = "Atmospheric variability, Cloud contamination, Data quality, High 
                         quality, Low qualities, MODIS NDVI, NDVI data, NDVI time series, 
                         Normalized difference vegetation index, Quality assessment, 
                         Science-data, Spatial and temporal correlation, Spatial temporals, 
                         Vegetation index, Geology, Noise abatement, Pixels, Quality 
                         control, Radiometers, Remote sensing, Time series, Spatial 
                         variables measurement.",
             abstract = "Normalized Difference Vegetation Index is a vegetation index 
                         widely applied in research. However, noise induced by cloud 
                         contamination and atmospheric variability affect the data quality. 
                         We propose the reconstruction of time series of MODIS NDVI data 
                         based on a quality assessment of the science data sets and on a 
                         spatial-temporal analysis of the low quality pixels. The MOD13Q1 
                         product was analyzed over a period of one year. The first task was 
                         to identify the pixels with the lowest guarantee of quality. The 
                         next step was to recalculate the NDVI values based on spatial and 
                         temporal correlations. The results indicate that the 
                         spatial-temporal information, combined with pixel quality 
                         assessment, is a promising method for reconstructing high-quality 
                         MODIS NDVI time series.",
  conference-location = "Munich",
      conference-year = "2012",
                  doi = "10.1109/IGARSS.2012.6350807",
                  url = "http://dx.doi.org/10.1109/IGARSS.2012.6350807",
                label = "lattes: 7176155601161528 1 OliveiraEpip:2012:NOREMO",
             language = "en",
         organisation = "Geoscience and Remote Sensing Society (GRS)",
           targetfile = "06350807.pdf",
        urlaccessdate = "30 abr. 2024"
}