Vegetable　production　is　important　because　of　the　food　security,　diet　improvement　and　socio-economic　value.　Mapping　the　location　and　extent　of　vegetable　fields　is　therefore　important　in　agricultural　policy,　food　security　and　farmer　support.　Dynamic　Time　Warping　(DTW)　is　a　common　way　to　map　crops　from　time　series　of　satellite　images.　However,　as　all　hard　classifications,　it　does　not　show　the　spatial　distribution　of　uncertainty　in　the　classification.　In　fuzzy　classification,　where　memberships　to　multiple　classes　are　assigned　to　each　pixel,　differ-ences　in　membership　between　the　first　and　the　runners-up　class　can　be　used　to　assess　classification　uncertainty　at　the　pixel　level.　This　research　formulates　a　fuzzy　classifier　based　upon　Time-Weighted　Dynamic　Time　Warping　(TWDTW)　distances　to　map　vegetable　types　from　time　series　of　Sentinel-1A　SAR　images.　For　each　pixel,　the　TWDTW　distances　to　the　classes　was　normalised　by　dividing　them　by　the　sum　of　all　TWDTW　distances　to　all　the　classes　for　that　pixel.　The　normalized　distances　were　then　used　to　compute　fuzzy　memberships　for　each　pixel　to　each　class,　using　the　Gaussian　membership　function.　Based　on　these　memberships,　fuzzy　measures　such　as　Confusion　Index　(CI),　Ambiguity　Index　(AI),　fuzziness　and　fuzzy　membership　were　calculated　and　different　thresholds　applied　on　each　of　the　measures　during　subsequent　defuzzification.　The　overall　accuracy　and　kappa　coefficient　of　the　defuzzified　output　results　were　0.86　and　0.83,　respectively,　which　was　an　improvement　with　regard　to　the　crisp　Time-Weighted　Dynamic　Time　Warping　with　SPRING　strategy　for　subsequence　searching　(TWDTWS)　algorithm　with　0.73　and　0.68　for　overall　accuracy　and　kappa,　respectively.　This　study　concludes　that　this　new　approach　improves　classification　accuracy　in　image　classification　by　excluding　pixels　with　high　un-certainty,　which　is　especially　relevant　when　only　a　limited　number　of　classes　are　sampled　and　mapped.