Xavier, AntonioFragoso, RuiCosta Freitas, M. B.Rosario, Maria do SocorroValente, Florentino2018-12-072018-12-072018-062073-445Xhttp://hdl.handle.net/10400.1/11512Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, which develops a supervised classification of satellite images by using a survey and empirical knowledge. To ensure the consistency with multiple sources of information, a minimum cross-entropy process was used. The proposed model was applied using two supervised classification algorithms and a more informative set of biophysical information. The results were validated and analyzed by considering various sources of information, showing that an entropy approach combined with supervised classifications may provide a reliable data disaggregation.engRemote sensing imagerySupervised classificationMaximum entropySpatial disaggregationDistribution MapsLand coverAllocationGisjournal article10.3390/land7020062