Intertidal　wetlands　are　the　transitional　zone　between　terrestrial　and　marine　ecosystems,　and　they　are　of　ecological　and　economic　importance.　However,　intertidal　wetlands　are　severely　damaged　due　to　natural　causes　(e.g.,　climate　change　and　sea-level　rise)　and　anthropogenic　causes　(e.g.,　coastal　reclamation　and　excessive　tourism　development).　Therefore,　tracking　the　spatiotemporal　changes　of　intertidal　wetlands　is　important　for　scientific　management　and　high-quality　development　of　coastal　areas.　Compared　with　traditional　surveying　methods,　remote　sensing　has　better　capacity　in　monitoring　intertidal　wetlands　dynamically　on　a　large　scale.　Acquiring　complete　information　of　intertidal　wetland　from　a　single-phase　remote　sensing　image　is　difficult　owing　to　the　influences　of　cloudy　weather　and　tidal　periodic　submergence.　The　problem　of　extracting　the　information　of　the　intertidal　zone　under　the　influences　of　dynamic　tidal　submerge　should　be　solved　for　the　application　of　remote　sensing　in　coastal　areas.In　this　study,　we　proposed　a　combined　method　using　the　time-series　remote　sensing　indices　and　the　geographic　characteristics　in　the　subtropical　intertidal　wetland　of　Fujian　Province,　China　on　the　basis　of　the　GEE　platform.　Three　main　types　of　intertidal　wetlands　including　high　marsh,　low　marsh,　and　tidal　flat　were　classified　by　the　following　steps.　First,　water　and　vegetation　indices　were　utilized　to　extract　water　bodies　and　vegetation　from　every　single　image.　Second,　the　water　and　vegetation　frequencies　derived　from　time-series　images　were　used　to　distinguish　the　high　marsh,　low　marsh,　and　tidal　flat　according　to　the　tidal　dynamics　and　vegetational　phonology.　Finally,　the　accuracy　of　the　results　was　verified　by　the　high-resolution　image　on　Google　Earth　Pro　and　in　situ　data.　The　results　were　compared　with　similar　datasets　to　assess　the　reliability　and　robustness　of　the　proposed　method.The　overall　classification　accuracy　was　97.47%,　and　the　Kappa　coefficient　was　0.96.　The　verifications　showed　misclassifications　in　the　transitional　area.　The　total　area　of　intertidal　wetlands　in　Fujian　Province　was　1061.3　km2,　and　the　areas　of　high　marsh,　low　marsh,　and　tidal　flat　were　18.1,　137.3,　and　905.8　km2,　respectively.　Intertidal　wetlands　were　concentrated　in　estuaries　and　bays.　The　area　of　tidal　flat　decreased　from　north　to　south　along　the　coast,　but　a　converse　trend　of　the　area　of　high　marsh　was　observed.　The　vegetation　was　mainly　distributed　in　the　southern　Quanzhou　Bay,　Jiulongjiang　Estuary,　and　Zhangjiang　Estuary,　and　it　was　less　in　northern　Fujian.　Comparing　the　results　of　this　study　with　similar　datasets　showed　that　our　study　improved　classification　accuracy　in　the　Fujian　Province.　However,　some　objective　factors　such　as　mixed　pixels　and　clouds　could　affect　the　accuracy　of　the　classification.This　research　developed　a　method　based　on　the　GEE　platform　and　time-series　remote　sensing　indices　to　classify　intertidal　wetlands　for　overcoming　the　dilemma　faced　by　single-phase　remote　sensing　images　in　the　intertidal　zone　information　extraction.　The　results　showed　certain　superiority　compared　with　similar　datasets　during　the　same　period.　The　method　reduced　the　impact　of　the　year-round　cloudy　and　rainy　weather　in　the　subtropical　coastal　zone　and　tidal　dynamics　effectively.　The　present　datasets　will　provide　important　basic　data　and　technical　supports　for　the　sustainable　management　and　utilization　of　coastal　resources　of　the　region.　©　2022,　Science　Press.　All　right　reserved.