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@Article{SanchesFAMLSPVM:2018:LEBeDa,
               author = "Sanches, Ieda Del'Arco and Feitosa, Raul Q. and Achanccaray, P. 
                         and Montibeller, Bruno and Luiz, Alfredo J. B. and Soares, M. Dias 
                         and Prudente, Victor Hugo Rohden and Vieira, Denis Corte and 
                         Maurano, Luis Eduardo Pinheiro",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and 
                         {Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Empresa 
                         Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "LEM benchmark database for tropical agricultural remote sensing 
                         application",
              journal = "International Archives of the Photogrammetry Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2018",
               volume = "42",
               number = "1",
                pages = "387--392",
                month = "Sept.",
             keywords = "Free available database, MultiSpectral Instrument, C-Band SAR 
                         data, Agricultural Mapping/Monitoring, Double Cropping Systems.",
             abstract = "The monitoring of agricultural activities at a regular basis is 
                         crucial to assure that the food production meets the world 
                         population demands, which is increasing yearly. Such information 
                         can be derived from remote sensing data. In spite of topics 
                         relevance, not enough efforts have been invested to exploit modern 
                         pattern recognition and machine learning methods for agricultural 
                         land-cover mapping from multi-temporal, multi-sensor earth 
                         observation data. Furthermore, only a small proportion of the 
                         works published on this topic relates to tropical/subtropical 
                         regions, where crop dynamics is more complicated and difficult to 
                         model than in temperate regions. A major hindrance has been the 
                         lack of accurate public databases for the comparison of different 
                         classification methods. In this context, the aim of the present 
                         paper is to share a multi-temporal and multi-sensor benchmark 
                         database that can be used by the remote sensing community for 
                         agricultural land-cover mapping. Information about crops in situ 
                         was collected in Lu{\'{\i}}s Eduardo Magalh{\~a}es (LEM) 
                         municipality, which is an important Brazilian agricultural area, 
                         to create field reference data including information about first 
                         and second crop harvests. Moreover, a series of remote sensing 
                         images was acquired and pre-processed, from both active and 
                         passive orbital sensors (Sentinel-1, Sentinel-2/MSI, 
                         Landsat-8/OLI), correspondent to the LEM area, along the 
                         development of the main annual crops. In this paper, we describe 
                         the LEM database (crop field boundaries, land use reference data 
                         and pre-processed images) and present the results of an experiment 
                         conducted using the Sentinel-1 and Sentinel-2 data.",
                  doi = "10.5194/isprs-archives-XLII-1-387-2018",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLII-1-387-2018",
                 issn = "0256-1840",
             language = "en",
           targetfile = "sanches_lem.pdf",
        urlaccessdate = "26 nov. 2020"
}


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