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Satellite imagery is the foremost source of information to analyze and monitor land covers in several time ranges, especially over large areas. However, it is not always either freely available or easily compatible for the final users due to the different resolutions offered by sensors onboard the satellite platforms. Crop classification is an important task to control and make decisions related to the agricultural practice and its regulation. However, it is not trivial, especially for extensive areas. Thus, this paper proposes a new approach for crop classification in large areas by a combined use of multi-temporal open-source remote sensing data from Sentinel-2 (S2) and Landsat-8 (L8) satellite platforms. Having to deal with different spatial and temporal resolutions, special spatial regions (called Tuplekeys) were created within a local nested grid to allow a proper integration between the data of both sensors. Temporal variation …