Detailed　information　regarding　the　status　and　dynamics　of　farmlands　is　essential　to　protect　food　security,　particularly　for　areas　with　a　limited　portion　of　farmlands　like　Fujian　province.　Since　Fujian　has　a　mountainous　terrain　and　subtropical　climate,　it　is　challenging　to　acquire　cloud-free　remote　sensing　images　to　extract　land　information　from　remote　sensing　images.　This　study　investigates　the　use　of　Google　Earth　Engine　(GEE)　to　monitor　farmland　changes　from　remote　sensing　images,　and　Geographical　Detectors　to　analyze　the　underlying　driving　factors　in　the　Fujian　province.　We　trained　an　online　Deep　Neural　Network　(DNN)　model　via　Google　Colaboratory,　and　then　applied　to　it　classify　land-use　types　from　Landsat　images　during　2000-2020　derived　from　the　GEE　platform.　The　obtained　changes　of　land-use　types　were　then　inputted　to　Geographical　Detectors,　integrated　with　population,　meteorological,　and　socio-economic　data,　to　analyze　their　driving　forces.　The　results　showed　that　the　proposed　method　produced　land　use　maps　at　intervals　of　three　years　with　higher　overall　accuracy　(0.91±0.01).　Based　upon　Geographical　Detectors,　we　found　that　the　farmland　changes　mainly　caused　by　factors　regarding　elevation,　population　and　slope,　followed　by　factors　of　soil　type,　temperature　and　gross　domestic　product　(GDP).　We　conclude　that　the　GEE　platform　combined　with　deep　learning　models　is　of　high　potential　to　extract　land　cover　and　multi-temporal　land　use　maps　over　large　regions,　and　the　Geographical　Detectors　are　suitable　for　analyzing　land　changes　using　remote　sensing　derived　products.　©　2021　IEEE.