Accurate　and　updated　information　on　winter　wheat　distribution　is　vital　for　food　security.　The　intra-class　variability　of　the　temporal　profiles　of　vegetation　indices　presents　substantial　challenges　to　current　time　series-based　approaches.　This　study　developed　a　new　method　to　identify　winter　wheat　over　large　regions　through　a　transformation　and　metric-based　approach.　First,　the　trend　surfaces　were　established　to　identify　key　phenological　parameters　of　winter　wheat　based　on　altitude　and　latitude　with　references　to　crop　calendar　data　from　the　agro-meteorological　stations.　Second,　two　phenology-based　indicators　were　developed　based　on　the　EVI2　differences　between　estimated　heading　and　seedling/harvesting　dates　and　the　change　amplitudes.　These　two　phenology-based　indicators　revealed　variations　during　the　estimated　early　and　late　growth　stages.　Finally,　winter　wheat　data　were　extracted　based　on　these　two　metrics.　The　winter　wheat　mapping　method　was　applied　to　China　based　on　the　250　m　8-day　composite　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　2-band　Enhanced　Vegetation　Index　(EVI2)　time　series　datasets.　Accuracy　was　validated　with　field　survey　data,　agricultural　census　data,　and　Landsat-interpreted　results　in　test　regions.　When　evaluated　with　653　field　survey　sites　and　Landsat　image　interpreted　data,　the　overall　accuracy　of　MODIS-derived　images　in　2012-2013　was　92.19%　and　88.86%,　respectively.　The　MODIS-derived　winter　wheat　areas　accounted　for　over　82%　of　the　variability　at　the　municipal　level　when　compared　with　agricultural　census　data.　The　winter　wheat　mapping　method　developed　in　this　study　demonstrates　great　adaptability　to　intra-class　variability　of　the　vegetation　temporal　profiles　and　has　great　potential　for　further　applications　to　broader　regions　and　other　types　of　agricultural　crop　mapping.　(C)　2016　International　Society　for　Photogrammetry　and　Remote　Sensing,　Inc.　(ISPRS).　Published　by　Elsevier　B.V.　All　rights　reserved.