@Article{GamaWiedBisp:2022:ReIoEf,
               author = "Gama, F{\'a}bio Furlan and Wiederkehr, Natalia Cristina and 
                         Bispo, Polyanna da Concei{\c{c}}{\~a}o",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Manchester}",
                title = "Removal of Ionospheric Effects from Sigma Naught Images of the 
                         ALOS/PALSAR-2 Satellite",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "4",
                pages = "e962",
                month = "Feb.",
             keywords = "ALOS/PALSAR-2, ENVI, FFT filtering, Ionospheric scintillation, 
                         SNAP.",
             abstract = "The monitoring of forest degradation in the Amazon through radar 
                         remote sensing methodologies has increased intensely in recent 
                         years. Synthetic aperture radar (SAR) sensors that operate in 
                         L-band have an interesting response for land use and land cover 
                         (LULC) as well as for aboveground biomass (AGB). Depending on the 
                         magnetic and solar activities and seasonality, plasma bubbles in 
                         the ionosphere appear in the equatorial and tropical regions; 
                         these factors can cause stripes across SAR images, which disturb 
                         the interpretation and the classification. Our article shows a 
                         methodology to filter these stripes using Fourier fast transform 
                         (FFT), in which a stop-band filter removes this noise. In order to 
                         make this possible, we used Environment for Visualizing Images 
                         (ENVI), Sentinel Application Platform (SNAP), and Interactive Data 
                         Language (IDL). The final filtered scenes were classified by 
                         random forest (RF), and the results of this classification showed 
                         superior performance compared to the original scenes, showing this 
                         methodology can help to recover historic series of L-band 
                         images.",
                  doi = "10.3390/rs14040962",
                  url = "http://dx.doi.org/10.3390/rs14040962",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-00962-v2.pdf",
        urlaccessdate = "29 jun. 2024"
}