A minimum cross entropy approach to disaggregate agricultural data at the field level

• Autor(a) principal: Xavier, Antonio
• Data de Publicação: 2018
• Outros Autores: Fragoso, Rui, Costa Freitas, M. B., Rosario, Maria do Socorro, Valente, Florentino
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/11512
• topic: Remote sensing imagery; Supervised classification; Maximum entropy; Spatial disaggregation; Distribution Maps; Land cover; Allocation; Gis

Agricultural 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.