@Article{ChenLMBDSSHLO:2018:MaCrCr,
               author = "Chen, Yaoliang and Lu, Dengsheng and Moran, Emilio and Batistella, 
                         Mateus and Dutra, Luciano Vieira and Sanches, Ieda Del'Arco and 
                         Silva, Ramon Felipe Bicudo da and Huang, Jingfeng and Luiz, 
                         Alfredo Jos{\'e} Barreto and Oliveira, Maria Antonia Falc{\~a}o 
                         de",
          affiliation = "{Zhejiang Agriculture and Forestry University} and {Zhejiang 
                         Agriculture and Forestry University} and {Michigan State 
                         Universit} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria 
                         (EMBRAPA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Zhejiang 
                         University} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria 
                         (EMBRAPA)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Mapping croplands, cropping patterns, and crop types using MODIS 
                         time-series data",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2018",
               volume = "69",
                pages = "133--147",
                month = "July",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura 
                         sustent{\'a}vel}",
             keywords = "Croplands, Cropping patterns, Crop types, MODIS NDVI, Decision 
                         tree classifier, Brazil.",
             abstract = "The importance of mapping regional and global cropland 
                         distribution in timely ways has been recognized, but separation of 
                         crop types and multiple cropping patterns is challenging due to 
                         their spectral similarity. This study developed a new approach to 
                         identify crop types (including soy, cotton and maize) and cropping 
                         patterns (Soy Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, 
                         Fallow-Cotton and Single crop) in the state of Mato Grosso, 
                         Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) 
                         normalized difference vegetation index (NDVI) time series data for 
                         2015 and 2016 and field survey data were used in this research. 
                         The major steps of this proposed approach include: (1) 
                         reconstructing NDVI time series data by removing the 
                         cloud-contaminated pixels using the temporal interpolation 
                         algorithm, (2) identifying the best periods and developing 
                         temporal indices and phenological parameters to distinguish 
                         croplands from other land cover types, and (3) developing crop 
                         temporal indices to extract cropping patterns using NDVI 
                         time-series data and group cropping patterns into crop types. 
                         Decision tree classifier was used to map cropping patterns based 
                         on these temporal indices. Croplands from Landsat imagery in 2016, 
                         cropping pattern samples from field survey in 2016, and the 
                         planted area of crop types in 2015 were used for accuracy 
                         assessment. Overall accuracies of approximately 90%, 73% and 86%, 
                         respectively were obtained for croplands, cropping patterns, and 
                         crop types. The adjusted coefficients of determination of total 
                         crop, soy, maize, and cotton areas with corresponding statistical 
                         areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research 
                         indicates that the proposed approach is promising for mapping 
                         large-scale croplands, their cropping patterns and crop types.",
                  doi = "10.1016/j.jag.2018.03.005",
                  url = "http://dx.doi.org/10.1016/j.jag.2018.03.005",
                 issn = "0303-2434",
             language = "en",
           targetfile = "chen_mapping.pdf",
        urlaccessdate = "27 abr. 2024"
}