As part of the G4AW SMARTSeeds project, this study aims to investigate the potential of dense time series of Sentinel- 1A dual polarization data for the classification of vegetables that are common in East Java, Indonesia. We first analyzed the temporal behavior of three main types of vegetables (i.e. chili, tomato and cucumber) in terms of backscatter (VH and VV) intensity, and of polarimetric features (i.e. entropy, al- pha and anisotropy) derived from polarization decomposition. We then applied a support vector machine with an intersection kernel to the time series data of vegetable samples collected in field. Our results showed that dense time series Sentinel-1A images are of high potential for vegetable classification. Be- sides using backscatter intensity, the polarimetric information can further improve the discrimination between three specific vegetable types.