Water　body　identifying　is　critical　to　climate　change,　water　resources,　ecosystem　service　and　hydrological　cycle.　Multi-layer　perceptron(MLP)　is　the　popular　and　classic　method　under　deep　learning　framework　to　detect　target　and　classify　image.　Therefore,　this　study　adopts　this　method　to　identify　the　water　body　of　Landsat8.　To　compare　the　performance　of　classification,　the　maximum　likelihood　and　water　index　are　employed　for　each　study　area.　The　classification　results　are　evaluated　from　accuracy　indices　and　local　comparison.　Evaluation　result　shows　that　multi-layer　perceptron(MLP)　can　achieve　better　performance　than　the　other　two　methods.　Moreover,　the　thin　water　also　can　be　clearly　identified　by　the　multi-layer　perceptron.　The　proposed　method　has　the　application　potential　in　mapping　global　scale　surface　water　with　multi-source　medium-high　resolution　satellite　data.　©　Authors　2018.