Tidal　flats　are　important　transitional　zones　between　terrestrial　and　marine　ecosystems　and　have　complicated　ecological　processes　and　essential　ecosystem　services.　Tidal　flats　are　highly　dynamic　under　the　influences　of　land-sea　interactions　and　anthropogenic　activities.　Limited　by　the　accessibility,　it　is　difficult　to　map　the　tidal　flat　using　traditional　survey.　To　solve　the　difficulty　in　obtaining　tidal　flat　elevation　data,　a　tidal　flat　elevation　inversion　model　suitable　for　large-scale　with　high　accuracy　is　needed.　In　this　study,　we　proposed　an　algorithm　incorporating　tidal　submergence　and　time-series　Remote　Sensing　(RS)　data　to　map　the　topography　of　tidal　flats.　We　used　Chongming　Dongtan　as　an　example　and　further　extended　the　results　to　the　whole　Yangtze　Estuary.　Firstly,　the　K-means++　clustering　was　employed　to　extract　the　inundation　extent　of　tidal.　Then,　the　frequency　of　tidal　inundation　of　each　pixel　was　calculated　from　the　time　series　RS　data.　Finally,　the　tidal　flat　topography　was　retrieved　based　on　the　regional　tidal　frequency.　All　available　Sentinel-2　and　Landsat-8　images　from　2016　to　2020　were　used　to　build　the　time-series　dynamic　of　tidal　flats　to　map　the　topography.　Verified　by　the　in-situ　data,　the　results　showed　that　the　total　accuracy　and　F1-score　of　the　inundation　extent　extraction　of　the　tidal　flats　were　97.73%　and　0.98,　respectively.　The　average　absolute　error　of　elevation　inversion　was　0.15　m.　The　accuracy　of　tidal　flat　elevation　was　positively　correlated　with　the　number　of　available　images.　The　total　area　of　tidal　flats　was　346.93　km　2　with　an　elevation　range　of　1.00~3.84　m.　The　tidal　flats　in　the　Yangtze　Estuary　were　mainly　distributed　in　Chongming　Dongtan,　Jiuduansha,　Hengsha　Dongtan,　Nanhui　Biantan,　and　Tuanjiesha.　Among　them,　Nanhui　beach　had　the　largest　area　(107.44　km　2),　while　Chongming　east　beach　had　the　largest　elevation　difference　(2.84　m).　The　distribution　status　of　tidal　flat　was　mainly　affected　by　sediment　hydrodynamics,　vegetation,　and　human　engineering　activities.　Compared　with　the　existing　dataset,　our　results　showed　a　more　robust　capacity　in　the　inundation　extent　extraction　of　tidal　flats.　With　the　increasing　number　of　effective　observations　and　tidal　level　information　from　time-series　RS　images　in　coastal　areas,　the　extraction　accuracy　of　tidal　flat　information　could　be　further　improved.　The　proposed　algorithm　has　a　great　potential　in　rapid　mapping　of　tidal　flat　topography　and　is　of　great　significance　for　the　dynamic　monitoring　and　management　of　tidal　flat　resources.　©　2022,　Science　Press.　All　right　reserved.