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Early-season crop mapping provides decision-makers with timely information on crop types and conditions that are crucial for agricultural management. Current satellite-based mapping solutions mainly rely on optical imagery, albeit limited by weather conditions. Very few exploit long-time series of polarized synthetic aperture radar (SAR) imagery. To address this gap, we assessed the performance of COSMO-SkyMed <italic>X</italic>-band dual-polarized (HH, VV) data in a test area in Ponte a Elsa (central Italy) in January&#x2013;September 2020 and 2021. A deep learning convolutional neural network (CNN) classifier arranged with two different architectures (1-D and 3-D) was trained and used to recognize ten classes. Validation was undertaken with <italic>in situ</italic> measurements from regular field campaigns carried out during satellite overpasses over more than 100 plots each year. The 3-D classifier structure and the combination of HH&#x002B;VV backscatter provide the best classification accuracy, especially during the first months of each year, i.e., 80&#x0025; already in April 2020 and in May 2021. Overall accuracy above 90&#x0025; is always marked from June using the 3-D classifier with HH, VV, and HH&#x002B;VV backscatter. These experiments showcase the value of the developed SAR-based early-season crop mapping approach. The influence of vegetation phenology, structure, density, biomass, and turgor on the CNN classifier using <italic>X</italic>-band data requires further investigations, along with the relatively low producer accuracy marked by vineyard and uncultivated fields.