Low-intensity farming systems are ofgreat importance for biodiversity in Europe, but they are often affected by soil degradation or economic pressure, leading to either land abandonment or intensification of agriculture. These changes in land use influence local biodiversity patterns and require annual monitoring of land cover. To accuratelymap landcover in such spatio-temporal complex landscapes, it is important to capture their phenological dynamics and fine spatial heterogeneity.Multi-seasonal analyses using optical sensorswith a mediumspatial resolution from 10 to 60 m (e.g. Landsat) have been used for this task, but data availability can be scarce due to cloud cover, sub-optimal acquisition schedules and data archive access restrictions. Combining coarse spatial resolution data from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat provides opportunities to close these gaps by simulating Landsat-like images at MODIS temporal resolution. In this study, we test whether and by what degree land cover maps of complex Mediterranean landscapes improve by integrating multi-seasonal Landsat imagery, as well as whether STARFM-simulated imagery can be used whenever original multi-seasonal Landsat observations are unavailable. Therefore, we develop different classification scenarios based on seasonally varying data availability and based on original and simulated Landsat data. Results show that multi-seasonal Landsat data from spring and early autumn are crucial for achieving satisfying mapping accuracies (overall accuracy 74.5%). Using synthetic Landsat imagery increases classification accuracy compared to using single-date Landsat data, but accuracies were never as good as a classification based on original data. We conclude that multi-seasonal data is essential for mapping complex Mediterranean landscapes and that STARFM can be used to compensate for missing Landsat observations. However, if Landsat data availability is sufficient to cover all phenologically important dates, we suggest relying solely on Landsat.