Due　to　its　ability　to　penetrate　the　cloud,　Synthetic　Aperture　Radar　(SAR)　has　been　a　great　resource　for　crop　mapping.　Previous　research　has　verified　the　applicability　of　SAR　imagery　in　object-oriented　crop　classification,　however,　speckle　noise　limits　the　generation　of　optimal　segmentation.　This　paper　proposed　an　innovative　SAR-based　maize　mapping　method　supported　by　optical　image,　Gaofen-1　PMS,　based　segmentation,　named　as　parcel-based　SAR　classification　assisted　by　optical　imagery-based　segmentation　(os-PSC).　Polarimetric　decomposition　was　applied　to　extract　polarimetric　parameters　from　multi-temporal　RADARSAT-2　data.　One　Gaofen-1　image　was　then　used　for　parcel　extraction,　which　was　the　basic　unit　for　SAR　image　analysis.　The　final　step　was　a　multi-step　classification　for　final　maize　mapping　including:　the　potential　maize　mask　extraction,　pure/mixed　maize　parcel　division　and　an　integrated　maize　map　production.　Results　showed　that　the　overall　accuracy　of　the　os-PSC　method　was　89.1%,　higher　than　those　of　pixel-level　classification　and　SAR-based　segmentation　methods.　The　comparison　between　optical-　and　SAR-based　segmentation　demonstrated　that　optical-based　segmentation　would　be　better　at　representing　maize　field　boundaries　than　the　SAR-based　segmentation.　Moreover,　the　parcel-　and　pixel-level　integrated　classification　will　be　suitable　for　many　agricultural　systems　with　small　landownership　where　inter-cropping　is　common.　Through　integrating　advantages　of　the　SAR　and　optical　data,　os-PSC　shows　promising　potentials　for　crop　mapping.　©　2018　Elsevier　B.V.