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This study aimed to map, separate, and estimate soya bean and corn crop areas in Paraná State, Brazil, in the harvest years 2012/13 and 2013/14, using the enhanced vegetation index (EVI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Thus, two methodologies were integrated, the first considering heterogeneity on the dates of crop cycles, the scenes required to generate images of minimum and maximum vegetation indexes, creating a colour composite red, green, and blue (RGB), and identifying two cultures simultaneously. In the second methodology, soya bean and corn were identified and mapped using the selection of pure pixels and the supervised classification algorithm Spectral Angle Mapper (SAM). In order to avoid overlapping areas, we multiplied the results from the first and second methodologies to obtain the final separation. The final validation of the mapping was compared to official data, identifying high correlation to crops. Based on Medium-Resolution Linear Imaging Self-Scanner (LISS-III) and Land Remote Sensing Satellite (Landsat-8) images, the similarity of global accuracy (GA) and kappa accuracy indices was determined, being classified as good and excellent, respectively. It showed that the use of the two consortium methodologies for separation and overlap elimination of these crops in the state of Paraná was efficient. [ABSTRACT FROM AUTHOR]

The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops. [ABSTRACT FROM AUTHOR]