Multiple　cropping　is　a　widespread　agricultural　intensification　for　increasing　food　production.　National-scale　Cropping　Intensity　(CI)　mapping　is　important　for　achieving　sustainable　development　goals.　However,　previous　studies　have　largely　relied　on　vegetation　indices　(VI)　waves　for　detecting　valid　cropping　cycles,　which　is　challenged　by　the　complexity　of　agricultural　systems.　i.e.　winter　crops　with　multiple　VI　waves　and　double　rice　with　less　distinctive　waves.　This　study　proposed　a　robust　framework　for　Mapping　cropping　Intensity　through　better-characterizing　crop　Life　cycles　based　on　combined　considerations　of　vegetative　and　productive　Stages　(MILS).　The　number　of　cropping　cycles　was　estimated　by　the　frequency　of　valid　coupled　patterns　of　vegetation　and　brownness　indices　from　Sentinel-2　(S2)　MultiSpectral　Instrument　(MSI)　time　series　and　further　improved　through　fusing　Sentinel-1　(S1)　SAR　and　S2　data　to　reduce　the　omission　errors　of　double　rice.　The　MILS　algorithm　was　implemented　using　the　Google　Earth　Engine　platform　and　applied　to　China,　which　is　dominated　by　smallholder　farms　with　diverse　cropping　patterns.　This　study　produced　the　first　10　m　updated　CI　map　over　conterminous　China　(CIChina10m)　in　2020　by　fusing　S1　and　S2　time　series.　The　CIChina10m　had　an　overall　accuracy　of　93.93%　when　validated　with　14,468　widely　spread　reference　sites.　The　cropping　intensity　was　1.2769　on　the　national　scale,　which　illustrated　higher　values　in　the　Middle　and　lower　reaches　of　the　Yangtze　River　plain　(CI　=　1.5879)　and　South　China　(CI　=　1.5503).　There　were　1086,620,　412,620　and　1,441　km2　areas　cultivated　by　single,　double　and　triple　cropping　in　China,　which　accounted　for　72.4%,　27.5%　and　0.1%　of　cropland,　respectively.　The　proposed　MILS　algorithm　showed　good　performances　in　detecting　complex　agricultural　systems,　which　can　be　further　applied　to　generate　continental　or　global　10-m　CI　products　with　good　quality.　Codes　of　the　MILS　algorithm　are　publicly　available　at　https://code.earthengine.google.com/a7f24f76291bf901ee25a130025a7ce6,　and　the　first　10m　national　CI　data　products　in　China　with　good　accuracy　(ChinaCI10m)　are　also　publicly　accessed　at　https://doi.org/10.6084/m9.figshare.23939505.　©　2023　International　Society　for　Photogrammetry　and　Remote　Sensing,　Inc.　(ISPRS)