Land cover mapping has become an increasingly important source of information in agriculture. Farmers use it for on-field decision-making, retailers and stock traders for planning and governments for making agricultural strategies and setting subsidy levels. Besides agricultural stakeholders, there are biologists and environmental scientists who use this kind of information for monitoring the quality of habitats.There are a number of optical EO satellites which offer images free of charge, with Sentinel-2, a part of ESA’s Copernicus programme, and Landsat-8, launched by NASA and USGS, being the most popular ones. This work focused on their application in land cover mapping in northern Serbia. Using joint information from these satellites we improved the system in many aspects. Data fusion allowed us to have images from more dates available. In this way we decreased the risk of misclassification due to missing data caused by high cloud coverage. Also, it allowed us to train a more accurate classifier compared to those trained on individual satellites. Finally, the spatial resolution of resulting maps was higher than the resolution of input images. This is extremely important for the observed region which is mostly constituted of small fields, 400 x 60 m in average. The database included more than 3 billion pixels with around 200 features each, i.e. all image channels from key dates between January and September. Classifiers were trained to distinguish between following crops: maize, wheat, sunflower, sugar beet and soybean, as well as forest and water bodies. Random forest proved to be the best classification algorithm, in terms of accuracy, speed and ability to deal with missing data. In classification of forest and water bodies, accuracy of up to 97 - 98% was achieved even without data fusion. However, since crop classification is a more difficult problem, performances of Sentinel and Landsat based classifiers could not match the performance of the joint classifier. Data fusion increased the overall system accuracy through the increase of average accuracy over all classes, as well as through more equal distribution of accuracy values over categories, in addition to higher spatial resolution of final decisions. The most significant improvement was observed in soybean classification, where Sentinel, Landsat and joint classifiers achieved accuracies of 84%, 87%, 89%, respectively. Other crops, such as sugar beet and wheat, which could be accurately classified with Sentinel and Landsat, were not improved further. This work is a step towards next year’s case, when besides these two satellites, Sentinel-2b will be available. It will cut the revisit time of Sentinels to only 6 days meaning that there will be even more data available and even better classification performance can be expected. The system developed in this research is intended to be a part of a broader geo service. This service would offer solutions customised for a vast variety of users, utilising the full potential of land cover mapping.