@Article{ChavesSanc:2023:ImCrMa,
               author = "Chaves, Michel Eust{\'a}quio Dantas and Sanches, Ieda Del'Arco",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improving crop mapping in Brazil's Cerrado from a data 
                         cubes-derived Sentinel-2 temporal analysis",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2023",
               volume = "32",
                pages = "e101014",
                month = "Nov.",
             keywords = "Agri-environmental policies, Analysis-ready datasets, Cerrado 
                         biome, Crops, Time series.",
             abstract = "The Sentinel-2/MultiSpectral Instrument (S2/MSI) expands the 
                         frequency of satellite observations, which is relevant to 
                         elaborate detailed and timely land use and land cover (LULC) 
                         classifications. However, storing, managing, and processing big 
                         data is costly and challenging, inducing a dimensionality 
                         reduction by modeling images as composite products. Contrastingly, 
                         it obliterates the temporal resolution improvement. As LULC 
                         changes are subtle over time, little is said about how much detail 
                         we lost by degrading temporal resolution. Data cube architectures 
                         enable storing, accessing, and modeling big data, mitigating 
                         losses. Brazil Data Cube (BDC) produces multidimensional data cube 
                         collections from different medium-resolution satellite data for 
                         Brazil, including S2/MSI. Here, we evaluated three BDC S2/MSI data 
                         cubes (two 16-day composites and one unblended, with the MSI 
                         original temporal resolution) to map a dynamic-and-representative 
                         region in the far-Western Bahia agricultural belt frontier, 
                         Cerrado biome, at crop type level. We incorporate spectral 
                         indices, ground samples, and crop calendars into a Random 
                         Forest-based temporal analysis. Overall accuracies (0.91 and 0.92 
                         for composites, and 0.96 reached for the unblended) highlight the 
                         S2/MSI temporal resolution for improving mapping tasks. Given the 
                         impact of the cropland frontier expansion over Cerrado in Brazil's 
                         commodity production, detecting subtle landscape variations can 
                         improve agri-environmental policies.",
                  doi = "10.1016/j.rsase.2023.101014",
                  url = "http://dx.doi.org/10.1016/j.rsase.2023.101014",
                 issn = "2352-9385",
             language = "en",
           targetfile = "1-s2.0-S2352938523000964-main.pdf",
        urlaccessdate = "07 maio 2024"
}