@Article{PrudenteSeOlXaXaAdSa:2022:MuApLa,
               author = "Prudente, Victor Hugo Rohden and Sergii, Skakun and Oldoni, Lucas 
                         Volochen and Xaud, Haron A. M. and Xaud, Maristela R. and Adami, 
                         Marcos and Sanches, Ieda Del'Arco",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Maryland} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria 
                         (EMBRAPA)} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria 
                         (EMBRAPA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Multisensor approach to land use and land cover mapping in 
                         Brazilian Amazon",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2022",
               volume = "189",
                pages = "95--109",
                month = "July",
             keywords = "Classification, Multilayer Perceptron, Random Forest, Roraima 
                         state, Sentinel images, t-Distributed Stochastic Neighbor 
                         Embedding.",
             abstract = "Remote sensing has an important role in the Land Use and Land 
                         Cover (LULC) mapping process worldwide. Combining spaceborne 
                         optical and microwave data is essential for accurate 
                         classification in areas with frequent cloud cover, such as 
                         tropical regions. In this study, we investigate the possible 
                         improvements, when SAR data is incorporated into the 
                         classification process along with optical data. We used 
                         MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the 
                         Roraima State, Brazil, in 2019. This State is located in a 
                         tropical area, where the cloud cover is frequent over the year. 
                         Cloud cover becomes substantial, especially during the May-August 
                         period when crops are grown. Twenty-nine scenarios involving a 
                         combination of optical- and SAR-based features, as well as times 
                         of data acquisition, were considered in this study. Our results 
                         showed that optical or SAR data used individually are not enough 
                         to provide accurate LULC mapping. The best results in terms of 
                         overall accuracy (OA) were achieved using metrics of 
                         multi-temporal surface reflectance and vegetation index (VI) for 
                         optical imagery, and values of backscatter coefficient in 
                         different polarizations and their ratios yielding an OA of 86.41 ± 
                         1.74%. Analysis of three periods of data (January to April, May to 
                         August, and September to December) used for classification allowed 
                         us to identify the optimal period for distinguishing specific 
                         classes. When comparing our LULC map with a LULC product derived 
                         within the MapBiomas project we observed that our method performed 
                         better to map annual and perennial crops and water classes. Our 
                         methodology provides a more accurate LULC for the Roraima State, 
                         and the proposed technique can be applied to benefit other regions 
                         that are affected by persistent cloud cover.",
                  doi = "10.1016/j.isprsjprs.2022.04.025",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2022.04.025",
                 issn = "0924-2716",
             language = "en",
           targetfile = "Prudente_2022_multisensor.pdf",
        urlaccessdate = "20 maio 2024"
}