Lapini, A.A.LapiniFontanelli, G.G.FontanelliPettinato, S.S.PettinatoSanti, E.E.SantiPaloscia, S.S.PalosciaTapete, DeodatoDeodatoTapeteCigna, FrancescaFrancescaCigna2021-04-272021-04-272020https://hdl.handle.net/20.500.13025/5883Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data. © 2020 IEEE.conference paper10.1109/IGARSS39084.2020.9323190https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101994936&doi=10.1109%2fIGARSS39084.2020.9323190&partnerID=40&md5=685bdd470ffa343783a12006c906a89c