Focused　on　studying　wildfire　and　driving　forces　across　various　ecosystems　on　a　large　scale,　this　paper　presents　a　novel　method　utilizing　the　google　earth　engine　(GEE)　platform.　Firstly,　the　fire　information　for　resource　management　system　database,　Sentinel-2　images　and　driving　factor　information　in　four　different　Chinese　ecosystems　were　accessed　on　online　via　GEE　platform.　Then,　the　differential　normalized　burn　ratio　was　extracted　from　Sentinel-2　images　to　sieve　fire　spots.　Three　machine　learning　algorithms,　namely　random　forest,　support　vector　machine　and　augmented　regression　tree,　were　used　to　classify　fire　locations.　Furthermore,　we　determined　the　optimal-performing　classification　algorithms　for　each　ecosystem　and　assessed　variable　importance.　The　results　showed　that　random　forest　performed　best　with　accuracy　exceeding　92%　among　the　three　machine　methods　and　the　fire　drivers　varied　significantly　among　four　different　ecosystems.　In　Changzhi　City　of　Shanxi　Province,　and　the　Great　Xing′an　Mountains　of　Inner　Mongolia,　population　distribution　and　maximum　temperature　were　identified　as　the　most　influential　drivers,　respectively.　While　for　Liangshan　Yi　Autonomous　Prefecture　in　Sichuan　Province　and　Ganzhou　City　in　Jiangxi　Province,　the　palmer　drought　index　and　soil　moisture　emerged　as　the　primary　drivers.　This　study　demonstrates　the　efficacy　of　the　proposed　GEE-based　method　in　studying　wildfire　and　driving　forces　across　different　ecosystems　in　large　scale　regions.　©　2024　Press　of　Shanghai　Scientific　and　Technical　Publishers.　All　rights　reserved.