A large-scale framework for deriving tidal flat topography from SWOT data

Tidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34–0.47 m and R² = 0.81–0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management.