Surface　water　mapping　is　essential　for　monitoring　climate　change,　water　resources,　ecosystem　services　and　the　hydrological　cycle.　In　this　study,　we　adopt　a　multilayer　perceptron　(MLP)　neural　network　to　identify　surface　water　in　Landsat　8　satellite　images.　To　evaluate　the　performance　of　the　proposed　method　when　extracting　surface　water,　eight　images　of　typical　regions　are　collected,　and　a　water　index　and　support　vector　machine　are　employed　for　comparison.　Through　visual　inspection　and　a　quantitative　index,　the　performance　of　the　proposed　algorithm　in　terms　of　the　entire　scene　classification,　various　surface　water　types　and　noise　suppression　is　comprehensively　compared　with　those　of　the　water　index　and　support　vector　machine.　Moreover,　band　optimization,　image　preprocessing　and　a　training　sample　for　the　proposed　algorithm　are　analyzed　and　discussed.　We　find　that　(1)　based　on　the　quantitative　evaluation,　the　performance　of　the　surface　water　extraction　for　the　entire　scene　when　using　the　MLP　is　better　than　that　when　using　the　water　index　or　support　vector　machine.　The　overall　accuracy　of　the　MLP　ranges　from　98.25-100%,　and　the　kappa　coefficients　of　the　MLP　range　from　0.965-1.　(2)　The　MLP　can　precisely　extract　various　surface　water　types　and　effectively　suppress　noise　caused　by　shadows　and　ice/snow.　(3)　The　1-7-band　composite　provides　a　better　band　optimization　strategy　for　the　proposed　algorithm,　and　image　preprocessing　and　high-quality　training　samples　can　benefit　from　the　accuracy　of　the　classification.　In　future　studies,　the　automation　and　universality　of　the　proposed　algorithm　can　be　further　enhanced　with　the　generation　of　training　samples　based　on　newly-released　global　surface　water　products.　Therefore,　this　method　has　the　potential　to　map　surface　water　based　on　Landsat　series　images　or　other　high-resolution　images　and　can　be　implemented　for　global　surface　water　mapping,　which　will　help　us　better　understand　our　changing　planet.