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@Article{SanchesFeDiSoLuScMa:2018:SeImAg,
               author = "Sanches, Ieda Del'Arco and Feitosa, Raul Queiroz and Diaz, Pedro 
                         Marco Achanccaray and Soares, Marinalva Dias and Luiz, Alfredo 
                         Jos{\'e} Barreto and Schultz, Bruno and Maurano, Luis Eduardo 
                         Pinheiro",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and Embrapa and 
                         Geoambiente and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Campo Verde database: seeking to improve agricultural remote 
                         sensing of tropical areas",
              journal = "IEEE Geoscience and Remote Sensing Letters",
                 year = "2018",
               volume = "15",
               number = "3",
                pages = "369--373",
                month = "Mar.",
             keywords = "Agricultural mapping/monitoring, double cropping systems, free 
                         available database, remote sensing, synthetic aperture radar 
                         (SAR), tropical agriculture.",
             abstract = "In tropical/subtropical regions, the favorable climate associated 
                         with the use of agricultural technologies, such as no tillage, 
                         minimum cultivation, irrigation, early varieties, desiccants, 
                         flowering inducing, and crop rotation, makes agriculture highly 
                         dynamic. In this letter, we present the Campo Verde agricultural 
                         database. The purpose of creating and sharing these data is to 
                         foster advancement of remote sensing technology in areas of 
                         tropical agriculture, primarily the development and testing of 
                         methods for crop recognition and agricultural mapping. Campo Verde 
                         is a municipality of Mato Grosso state, localized in the Cerrado 
                         (Brazilian Savanna) biome, in central west Brazil. Soybean, maize, 
                         and cotton are the primary crops cultivated in this region. Double 
                         cropping systems are widely adopted in this area. There is also 
                         livestock and forestry production. Our database provides the 
                         land-use classes for 513 fields by month for one Brazilian crop 
                         year (between October 2015 and July 2016). This information was 
                         gathered during two field campaigns in Campo Verde (December 2015 
                         and May 2016) and by visual interpretation of a time series of 
                         Landsat-8/Operational Land Imager (OLI) images using an 
                         experienced interpreter. A set of 14 preprocessed synthetic 
                         aperture radar Sentinel-1 and 15 Landsat-8/OLI mosaic images is 
                         also made available. It is important to promote the use of radar 
                         data for tropical agricultural applications, especially because 
                         the use of optical remote sensing in these regions is hindered by 
                         the high frequency of cloud cover. To demonstrate the utility of 
                         our database, results of an experiment conducted using the 
                         Sentinel-1 data set are presented.",
                  doi = "10.1109/LGRS.2017.2789120",
                  url = "http://dx.doi.org/10.1109/LGRS.2017.2789120",
                 issn = "1545-598X",
             language = "en",
           targetfile = "sanches_campo.pdf",
        urlaccessdate = "23 nov. 2020"
}


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