In　recent　years,　the　use　of　deep　learning　in　remote　sensing　domain　has　made　it　possible　to　automate　mapping　in　large-scale.　In　this　paper,　we　propose　a　transfer　learning　method　which　pre-train　a　convolutional　neural　network　(CNN)　with　middle-resolution　remote　sensing　data　in　2016,　and　fine-tune　it　in　following　years　with　a　spot　of　high-resolution　remote　sensing　data　in　2017.　We　used　the　fine-tuned　model　to　mapping　the　early-rice　in　25　countries　which　cost　only　21　minutes,　and　yielded　an　overall　accuracy　of　81.68%.　The　result　demonstrate　that　the　convolutional　neural　network　model　can　transfer　in　different　time　period　with　little　adjustment　in　a　very　high　accuracy.　©　2018　IEEE.