 The use of remote sensing for crop mapping in Brazil has led to the development of a method that uses Landsat 8, Sentinel-2, SRTM+, and field data from 2016 to 2018 to identify cropped areas with a minimum global accuracy of 90% using simple non-iterative clustering segmentation and continuous naive base classifier. The results show correlations of 0.96 and agreement coefficients no lower than 0.86, ensuring mapping quality when using Sentinel and all Landsat imagery on the GE platform.