A pixel-based cropland classification study based on the fusion of data from satellite images with different resolutions is presented. It is based on a time series of multispectral images acquired at different resolutions by different imaging instruments, Landsat-8 and RapidEye. The proposed data fusion method capabilities are explored with the aim of overcoming the shortcomings of different instruments in the particular cropland classification scenario characterized by the very small size of crop fields over the chosen agricultural region situated in the plains of Vojvodina in northern Serbia. This paper proposes a data fusion method that is successfully utilized in combination with arobust random forest classifier in improving the overall classification performance, as well as in enabling application of satellite imagery with a coarser spatial resolution in the given specific cropland classification task. The developed method effectively exploits available data and provides an improvement over the existing pixel-based classification approaches through the combination of different data sources. Another contribution of this paper is the employment of crowdsourcing in the process of reference data collection via dedicated smartphone application.