Intertidal　mudflats　are　an　important　component　of　the　coastal　geomorphological　system　at　the　interface　between　ocean　and　land.　Accurate　and　up-to-date　mapping　of　intertidal　topography　at　high　spatial　resolution,　and　tracking　of　its　changes　over　time,　are　essential　for　coastal　habitat　protection,　sustainable　management　and　vulnerability　analysis.　Compared　with　ground-based　or　airborne　terrain　mapping,　the　satellite-based　waterline　method　is　more　cost-effective　for　constructing　large-scale　intertidal　topography.　However,　the　accuracy　of　the　waterline　method　is　affected　by　the　extraction　of　waterlines　and　the　calibration　of　waterline　height.　The　blurred　boundary　between　turbid　water　and　mudflats　in　the　tide-dominated　estuary　brings　enormous　challenges　in　accurate　waterline　extraction,　and　the　errors　in　estuarine　water　level　simulations　prevent　the　direct　calibration　of　waterline　heights.　To　address　these　issues,　this　paper　developed　a　novel　deep　learning　method　using　a　parallel　self-attention　mechanism　and　boundary-focused　hybrid　loss　to　extract　turbid　estuarine　waterlines　accurately　from　dense　Sentinel-2　time　series.　UAV　photogrammetric　surveys　were　employed　to　calibrate　waterline　heights　rather　than　the　simulated　water　levels,　such　that　the　error　propagation　is　constrained　effectively.　Annual　intertidal　topographic　maps　of　the　Yangtze　estuary　in　China　were　generated　from　2020　to　2022　using　the　optimized　waterline　method.　Experimental　results　demonstrate　that　the　proposed　deep　learning　method　could　achieve　excellent　performance　in　land　and　water　segmentation　in　time-varying　tidal　environments,　with　better　generalization　capability　compared　with　benchmark　U-Net,　U-Net++　and　U-Net+++　models.　The　comparison　between　the　generated　topography　and　UAV　photogrammetric　observations　resulted　in　an　RMSE　of　13　cm,　indicating　the　effectiveness　of　the　optimized　waterline　method　in　monitoring　morphological　changes　in　estuarine　mudflats.　The　generated　topographic　maps　successfully　identified　hotspots　of　mudflat　erosion　and　deposition.　Specifically,　the　mudflats　connected　to　the　land　predominantly　experienced　deposition　of　10-20　cm　over　the　twoyear　period,　whereas　the　offshore　sandbars　exhibited　instability　and　significant　erosion　of　20-60　cm　during　the　same　period.　These　topographic　maps　serve　as　valuable　datasets　for　providing　scientific　baseline　information　to　support　coastal　management　decisions.