Ecological　catastrophes　occurred　in　China＇s　coastal　region　as　a　result　of　rapid　invasion　of　Spartina　alterniflora.　It＇s　critical　to　monitor　the　spatiotemporal　dynamics　of　Spartina　alterniflora.　Remote　sensing　has　a　superior　ability　to　monitor　the　dynamics　of　intertidal　wetland　vegetation　on　a　broad　scale　as　compared　to　traditional　surveying　techniques.　However,　it　is　of　great　challenges　to　precisely　determine　the　amount　of　Spartina　alterniflora　using　a　single-phase　image　due　to　the　spectral　similarities　of　vegetation　with　various　species.　The　extensive　remote　sensing　data　still　need　to　be　further　explored　in　order　to　better　understand　the　temporal　and　geographical　characteristics　of　vegetation.　The　phonological　cycle　of　the　plant　can　be　tracked　using　the　time　series　remote　sensing　data,　which　can　then　provide　additional　temporal　features　for　vegetation　classification.　However,　the　impacts　of　cloudy　weather　and　tides　in　intertidal　zones　make　it　challenging　to　extract　the　phenological　parameters,　even　though　previous　phenology-based　algorithms　can　reduce　misclassification　caused　by　spectral　similarity.　In　this　study,　we　suggested　a　method　to　reduce　the　impact　of　cloudy　weather　and　tides　on　time　series　remote　sensing　data　by　integrating　the　Maximum-value　composite　algorithm　with　Savitzky-Golay　(S-G)　filter.　Then,　based　on　the　NDVI　time　series　derived　from　archived　Sentinel-2　data,　the　two-term　Fourier　function　is　used　to　determine　the　optimal　time　window　and　phenological　parameters　for　the　classification　of　Spartina　alterniflora.　The　landscape　of　Fujian＇s　coastal　zone　and　the　distribution　of　Spartina　alterniflora　were　finally　mapped　using　a　random　forest　classifier　on　the　Google　Earth　Engine　(GEE)　platform.　The　results　revealed　that,　with　an　overall　accuracy　of　89.81%　and　a　Kappa　coefficient　of　0.88,　respectively,　the　beginning　of　the　growing　season　(June　to　July)　is　the　optimum　time　window　for　classifying　intertidal　vegetation.　It　demonstrates　how　effectively　this　technology　may　be　used　to　monitor　Spartina　alterniflora　on　a　broad　scale　in　coastal　areas.　According　to　the　results,　Spartina　alterniflora　had　a　total　area　of　roughly　100.78　km2　in　Fujian＇s　coastal　zone　in　2020,　with　the　most　of　it　being　concentrated　in　Ningde,　Fuzhou,　Quanzhou,　and　Zhangzhou.　The　largest　patches　of　Spartina　alterniflora,　with　a　distribution　of　37.79%,　were　found　in　Ningde.　Due　to　the　coastal　geomorphology,　Spartina　alterniflora　displayed　a　large　diversity　in　its　spatial　distribution　along　the　Fujian　coast.　The　demonstration　in　Fujian　Province　shows　that　the　suggested　method　can　offer　significant　potential　for　long-term　and　extensive　spatial　scale　monitoring　of　Spartina　alterniflora　dynamics,　which　could　assist　coastal　high-quality　and　sustainable　development.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.