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Tidal flats, which serve as a crucial transitional zone from land to sea, provides valuable habitats, potential land resources, and buffers against marine hazards. However, accurately and efficiently mapping high-frequency tidal flat topography poses challenges only relying on single-source data, which provides limited available imagery. In this study, we explored the integration of free-access multi-source optical imagery and ICESat-2 laser lidar data to derive annual tidal flat topography. Specifically, we established the relationship between inundation frequency and elevations based on two regression models: the simple linear model (SLM) and the third-order polynomial model (TPM). We found that the SLM model outperformed the TPM, which tended to overestimate elevations in areas with lower elevations. The proposed method was applied to China’s largest tidal flat (along Jiangsu central coast) with complex hydrodynamic conditions, achieving a root mean square error of 0.35 m compared to high-accuracy airborne lidar data. Based on the calibrated SLM model, we generated tidal flat topography covering 1749.89 km2 from a satellite-derived inundation frequency map, with elevations ranging from – 0.81–2.96 m (elevation difference: 3.77 m). This satellite-based method advances our understanding of coastal dynamics, and support data-driven decision-making for sustainable coastal development.
Xin et al. (Sun,) studied this question.