Data for "A Universal Framework for Long-Term Tidal Flat Elevation Mapping Using Robust Water-Land Segmentation and Homogeneous Inundation Frequency"

Accurate long-term tidal flat elevation mapping is crucial for understanding coastal evolution processes, assessing disaster risks, and supporting ecological management. However, prevailing approaches typically rely on independent modeling for each temporal snapshot, resulting in inefficiencies and discontinuities in temporal coverage. To overcome these limitations, this study proposes a universal framework for long-term annual tidal flat elevation mapping, utilizing a robust water–land segmentation ruleset and statistically homogeneous annual inundation frequency (AIF) maps. The key methodological enhancements are: (1) a spatiotemporally robust water-land segmentation ruleset that achieves over 95% classification accuracy, thereby enabling efficient and reliable delineation; (2) a phase–tide–based weighted inversion algorithm that ensures statistical homogeneity across years of AIF maps, thereby enabling the construction of a temporally unified AIF–elevation model and eliminating repetitive annual modeling. The framework was validated across five representative coastal regions in China: the Yellow River Delta, Subei Bank, Yangtze River Estuary, Sansha Bay, and Lianzhou Bay, yielding integrated RMSE values of 0.20 m, 0.48 m, 0.30 m, 0.63 m, and 0.21 m, respectively, based on pooled elevation data from 2019 to 2024. Systematic evaluations confirmed the framework’s adaptability and reliability across diverse coastal environments.