Tidal flat topography mapping with Sentinel time series using cross-modal sample transfer and deep learning

Tidal flats are crucial components of coastal geomorphic systems, where the ocean meets the land. Timely and accurate topographic maps of tidal flats are essential for sustainable coastal management and development. Although satellite imagery-based inversion methods offer a cost-effective solution for constructing large-scale intertidal topography, their accuracy remains heavily dependent on the availability and quality of satellite data. Frequent cloudy and rainy weather in coastal areas presents significant challenges for extracting waterlines from optical images. To address these challenges, this study developed an integrated framework that leverages the complementary strengths of optical and Synthetic Aperture Radar (SAR) imagery, providing an innovative solution to accurately map tidal flat topographies at high spatial resolution. By utilizing the high-precision spatiotemporal distribution results of tidal flats extracted from optical images and integrating tidal constraints and temporal conditions, a cross-modal sample transfer strategy for Optical-SAR imagery was designed, which automatically generates a pseudo-sample library for SAR images. To optimize the automatic extraction of tidal flats in complex SAR imagery environments, we constructed a hybrid semantic segmentation network, UCTCNet. UCTCNet combines the local feature extraction capabilities of convolutional neural networks with the global information focus provided by attention mechanisms. ICESat-2 data was used as altimetry input based on the relationship between tidal flat elevations and inundation frequencies, which was combined with derived inundation frequency maps, low-tide imagery, and spectral indices to accurately invert tidal flat elevations using a random forest algorithm. Experimental results showed that the UCTCNet model demonstrated high potential in processing single-channel, high-noise, weak-feature Sentinel-1 SAR imagery, achieving an IoU of over 0.90, indicating strong performance in extracting high-level semantic features of tidal flats. The elevation inversion framework was validated along the entire coastal region of Jiangsu, China, for multi-temporal and multi-scene analysis. Further validation using generated topographic maps from unmanned aerial vehicle photogrammetry showed superior performance (RMSE = 0.24 m) compared to existing public tidal flat elevation data. The framework was also applied to derive DEMs from 2019 to 2023, revealing significant spatial and elevation changes in the North Jiangsu Radial Sand Ridges. The results further demonstrated the influence of various features, including inundation frequency maps, low-tide imagery, and spectral indices, on elevation inversion accuracy. The integration of S1 SAR data not only improved inversion accuracy but also helped address the limitations associated with discrete frequency data. These findings demonstrate that our proposed framework offers novel insights into high-resolution, large-scale mapping of tidal flat topography.