Remote　sensing　can　be　used　to　monitor　cropland　phenological　characteristics;　however,　tradeoffs　between　the　spatial　and　temporal　resolutions　of　cloudless　satellite　images　limit　the　accuracy　of　their　retrieval.　In　this　study,　an　improved　enhanced　spatial　and　temporal　adaptive　reflectance　fusion　model　(ESTARFM)　was　applied　to　human-dominated　Xiong＇an　New　Area　to　develop　a　self-adapting　algorithm　automating　the　extraction　of　main　phenological　transition　points　(greenup,　maturity,　senescence,　and　dormancy).　The　analyses　of　cropland　phenological　characteristics　were　performed　utilizing　the　Softmax　classification　method.　By　examining　three　different　phases　of　fusion　images,　it　was　found　that　the　improved　ESTARFM　was　more　accurate　than　the　original　ESTARFM　(correlation　coefficient　＞　0.76;　relative　root　mean　square　error　＜　0.25;　structural　similarity　index　＞　0.79).　The　reconstructed　normalized　difference　vegetation　indexes　were　consistent　with　that　acquired　by　the　Moderate　Resolution　Imaging　Spectroradiometer　(average　discrepancy:　0.1136,　median　absolute　deviation:　0.0110).　The　greenup,　maturity,　senescence,　and　dormancy　points　were　monitored　in　5-day　resolution　and　50-day　length　on　a　30-m　grid　scale,　and　their　average　day　of　year　(DOY)　were　67,　119,　127,　and　166　for　wheat;　173,　224,　232,　and　283　for　single-season　corn;　and　189,　227,　232,　and　285　for　rotation　corn,　respectively.　The　corresponding　median　absolute　deviations　were　2,　3,　2,　and　2　days　for　wheat;　2,　5,　3,　and　4　days　for　single-season　corn;　and　2,　5,　2,　and　2　days　for　rotation　corn,　respectively,　while　all　coefficients　of　variation　did　not　exceed　6%.　The　proposed　self-adapting　approach　can　be　used　for　identifying　the　planting　modes　at　grid　level　in　rotation　agroecosystems　and　cropland　phenological　dynamics　on　a　global　or　regional　scale.