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@Article{OliveiraEpipRenn:2014:SpAnEs,
               author = "Oliveira, Julio Cesar de and Epiphanio, Jos{\'e} Carlos Neves and 
                         Renn{\'o}, Camilo Daleles",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Window regression: A spatial-temporal analysis to estimate pixels 
                         classified as low-quality in MODIS NDVI time series",
              journal = "Remote Sensing",
                 year = "2014",
               volume = "6",
               number = "4",
                pages = "3123--3142",
                month = "Apr.",
             keywords = "Akaike information criterion, Data quality, Land use/land cover 
                         change, Mean absolute percentage error, Moderate resolution 
                         imaging spectroradiometer datum, MODIS, Normalized difference 
                         vegetation index, Spatial temporals, Deforestation, Estimation, 
                         Iterative methods, Noise abatement, Quality control, Radiometers, 
                         Regression analysis, Satellite imagery, Time series, Time series 
                         analysis, Pixels.",
             abstract = "MODerate resolution Imaging Spectroradiometer (MODIS) data are 
                         largely used in multitemporal analysis of various Earth-related 
                         phenomena, such as vegetation phenology, land use/land cover 
                         change, deforestation monitoring, and time series analysis. In 
                         general, the MODIS products used to undertake multitemporal 
                         analysis are composite mosaics of the best pixels over a certain 
                         period of time. However, it is common to find bad pixels in the 
                         composition that affect the time series analysis. We present a 
                         filtering methodology that considers the pixel position (location 
                         in space) and time (position in the temporal data series) to 
                         define a new value for the bad pixel. This methodology, called 
                         Window Regression (WR), estimates the value of the point of 
                         interest, based on the regression analysis of the data selected by 
                         a spatial-temporal window. The spatial window is represented by 
                         eight pixels neighboring the pixel under evaluation, and the 
                         temporal window selects a set of dates close to the date of 
                         interest (either earlier or later). Intensities of noises were 
                         simulated over time and space, using the MOD13Q1 product. The 
                         method presented and other techniques (4253H twice, Mean Value 
                         Iteration (MVI) and Savitzky-Golay) were evaluated using the Mean 
                         Absolute Percentage Error (MAPE) and Akaike Information Criteria 
                         (AIC). The tests revealed the consistently superior performance of 
                         the Window Regression approach to estimate new Normalized 
                         Difference Vegetation Index (NDVI) values irrespective of the 
                         intensity of the noise simulated. © 2014 by the authors; licensee 
                         MDPI, Basel, Switzerland.",
                  doi = "10.3390/rs6043123",
                  url = "http://dx.doi.org/10.3390/rs6043123",
                 issn = "2072-4292",
                label = "scopus 2014-05 OliveiraEpipRenn:2014:SpAnEs",
             language = "en",
           targetfile = "remotesensing-06-03123.pdf",
        urlaccessdate = "28 mar. 2024"
}


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