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@Article{ZhuLuVictDutr:2016:MaFrCr,
               author = "Zhu, Changming and Lu, Dengsheng and Victoria, Daniel and Dutra, 
                         Luciano Vieira",
          affiliation = "{Jiangsu Normal University} and {Michigan State University} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapping fractional cropland distribution in Mato Grosso, Brazil 
                         using time series MODIS enhanced vegetation index and Landsat 
                         Thematic Mapper data",
              journal = "Remote Sensing",
                 year = "2016",
               volume = "8",
               number = "1",
                pages = "Number 22",
             keywords = "Crop phenology analysis, Fractional cropland distribution, 
                         Landsat, Mato grosso, MODIS EVI, Seasonal dynamic index.",
             abstract = "Mapping cropland distribution over large areas has attracted great 
                         attention in recent years, however, traditional pixel-based 
                         classification approaches produce high uncertainty in cropland 
                         area statistics. This study proposes a new approach to map 
                         fractional cropland distribution in Mato Grosso, Brazil using time 
                         series MODIS enhanced vegetation index (EVI) and Landsat Thematic 
                         Mapper (TM) data. The major steps include: (1) remove noise and 
                         clouds/shadows contamination using the Savizky-Gloay filter and 
                         temporal resampling algorithm based on the time series MODIS EVI 
                         data; (2) identify the best periods to extract croplands through 
                         crop phenology analysis; (3) develop a seasonal dynamic index 
                         (SDI) from the time series MODIS EVI data based on three key 
                         stages: sowing, growing, and harvest; and (4) develop a regression 
                         model to estimate cropland fraction based on the relationship 
                         between SDI and Landsat-derived fractional cropland data. The root 
                         mean squared error of 0.14 was obtained based on the analysis of 
                         randomly selected 500 sample plots. This research shows that the 
                         proposed approach is promising for rapidly mapping fractional 
                         cropland distribution in Mato Grosso, Brazil.",
                  doi = "10.3390/rs8010022",
                  url = "http://dx.doi.org/10.3390/rs8010022",
                 issn = "2072-4292",
             language = "en",
           targetfile = "zhu_mapping.pdf",
        urlaccessdate = "01 dez. 2020"
}


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