In the framework of ERMES project (FP7 66983), an algorithm for mapping rice cultivation extents using mediumhigh resolution satellite data was developed. ERMES (An Earth obseRvation Model based RicE information Service) aims to develop a prototype of downstream service for rice yield modelling based on a combination of Earth Observation and in situ data. The algorithm was designed as a set of rules applied on a time series of Landsat 8 images, acquired throughout the rice cultivation season of 2014 from the plain of Thessaloniki, Greece. The rules rely on the use of spectral indices, such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Seasonal Wetness Index (NSWI), extracted from the Landsat 8 dataset. The algorithm is subdivided into two phases: a) a hard classification phase, resulting in a binary map (rice/no-rice), where pixels are judged according to their performance in all the images of the time series, while index thresholds were defined after a trial and error approach; b) a soft classification phase, resulting in a fuzzy map, by assigning scores to the pixels which passed (as ‘rice’) the first phase. Finally, a user-defined threshold of the fuzzy score will discriminate rice from no-rice pixels in the output map. The algorithm was tested in a subset of Thessaloniki plain against a set of selected field data. The results indicated an overall accuracy of the algorithm higher than 97%. The algorithm was also applied in a study are in Spain (Valencia) and a preliminary test indicated a similar performance, i.e. about 98%. Currently, the algorithm is being modified, so as to map rice extents early in the cultivation season (by the end of June), with a view to contribute more substantially to the rice yield prediction service of ERMES. Both algorithm modes (late and early) are planned to be tested in extra Mediterranean study areas, in Greece, Italy, and Spain.