Timely　and　accurate　mapping　of　paddy　rice　cultivation　is　needed　for　maintaining　sustainable　rice　production,　ensuring　food　security,　and　monitoring　water　usage.　Synthetic　Aperture　Radar　(SAR)　remote　sensing　plays　an　important　role　in　the　continuous　monitoring　and　mapping　of　rice　cultivation　in　cloudy　regions　since　it　is　not　affected　by　weather　conditions.　To　date,　most　SAR　imagery-based　rice　mapping　methods　rely　on　prior　knowledge　(e.g.,　the　planting　date)　and　empirical　thresholds　for　specific　regions,　which　limits　their　applications　in　large　spatial　scales.　To　tackle　this　limitation,　this　study　proposed　a　new　SAR-based　Paddy　Rice　Index　(SPRI)　to　quantify　the　probability　of　land　patches　planted　paddy　rice.　SPRI　fully　uses　unique　features　of　paddy　rice　during　the　transplanting-vegetative　period　in　the　Sentinel-1　VH　backscatter　time　series.　With　the　assistance　of　cloud-free　Sentinel-2　images,　SPRI　can　be　calculated　for　each　cropland　object　with　adaptive　parameters.　Then,　SPRI　values　of　cropland　objects　can　be　converted　to　paddy　rice　maps　using　the　binary-classification　threshold.　The　proposed　SPRI　method　was　tested　at　five　sites　with　diverse　climate　conditions,　landscape　complexity　and　cropping　systems.　Results　show　that　the　SPRI　was　able　to　produce　an　accurate　classification　map　with　an　overall　accuracy　of　over　88%　and　an　F1　score　of　over　0.86　at　all　sites.　Compared　with　the　existing　SAR-based　rice　mapping　methods,　our　method　performed　much　better　in　heterogeneous　agricultural　areas　where　rice　is　mosaiced　with　other　crops.　As　SPRI　does　not　need　any　prior　knowledge,　reference　samples　and　many　predefined　parameters,　it　has　high　flexibility　and　applicability　to　support　paddy　rice　mapping　in　large　areas,　especially　for　cloudy　regions　where　optical　remote　sensing　data　is　often　not　available.