@Article{ChavesSoarSancFron:2021:CBDaCu,
               author = "Chaves, Michel Eust{\'a}quio Dantas and Soares, Anderson R. and 
                         Sanches, Ieda Del Arco and Fronza, Jos{\'e} Guilherme",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Cognizant 
                         Technology Solutions} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "CBERS data cubes for land use and land cover mapping in the 
                         Brazilian Cerrado agricultural belt",
              journal = "International Journal of Remote Sensing",
                 year = "2021",
               volume = "42",
               number = "21",
                pages = "8398--8432",
                month = "Nov.",
             abstract = "The agricultural frontier expansion in the Cerrado biome made 
                         Brazil a leader in commodity exports and is changing its 
                         landscape. Hence, efforts to accurate land use and land cover 
                         (LULC) monitoring in this region are strategic, due to its role in 
                         Brazil's food, environmental, and economic security policy. 
                         Thinking on planning and technical sovereignty in the spatial 
                         sector, the China-Brazil Earth Resources Satellite (CBERS) Program 
                         was launched to provide useful data for decision-makers to manage 
                         the Brazilian territory independently of external policies. Their 
                         data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are 
                         largely applied in deforestation monitoring by remote sensing 
                         specialists but less applied than data from other image providers 
                         for machine learning-based LULC mapping due to the small number of 
                         spectral bands and limitations related to clouds and shadows 
                         detection. However, with advances in orbital data analysis, data 
                         cubes enabled storing and accessing large spatio-temporal 
                         analysis-ready data. Within this scope, the Brazil Data Cube 
                         Project (BDC) creates multidimensional data cubes from orbital 
                         sensors' data for all Brazilian territory. We applied BDC 
                         CBERS-4/WFI data cubes to generate LULC classifications for the 
                         Extremo Oeste Baiano agricultural belt correspondent to the 
                         2017/2018 and 2019/2020 harvest periods, at two levels of detail: 
                         broad and crop type, incorporating ground truth samples, crop 
                         calendar knowledge, and vegetation indices to a dense time series 
                         analysis approach. Overall Accuracies were equal to 0.87 and 0.89 
                         for broad, and 0.91 and 0.94 for crop type classifications. The 
                         results indicate CBERS-4/WFI data cubes as a useful tool for 
                         improving crop monitoring in the Cerrado biome based on machine 
                         learning.",
                  doi = "10.1080/01431161.2021.1978584",
                  url = "http://dx.doi.org/10.1080/01431161.2021.1978584",
                 issn = "0143-1161",
             language = "en",
           targetfile = "chaves_cbers.pdf",
        urlaccessdate = "02 maio 2024"
}