Integrating Temporal Vegetation and Inundation Dynamics for Elevation Mapping Across the Entire Turbid Estuarine Intertidal Zones Using ICESat-2 and Sentinel-2 Data

High-precision elevation mapping is essential for ecological restoration, marine disaster assessment, and morphodynamic simulation in intertidal zones. Current methodologies are often impeded by an over-reliance on extensive in situ measurements and are typically applicable only to regions devoid of vegetation. In this study, we first propose a novel method for spatially continuous elevation mapping of large-scale muddy intertidal zones within highly turbid estuaries, utilizing features at pixel, neighborhood, and temporal scales from satellite multispectral images. This method utilizes a random forest (RF) to model the relationships between elevations from Ice, Cloud, and Elevation Satellite 2 (ICESat-2) and band, texture, and index features from Sentinel-2, without relying on any supplementary in situ measurements. The innovation and strength of the proposed method lie in the simultaneous incorporation of two temporal features: vegetation occurrence frequency and water inundation frequency. These two features effectively utilize the variations observed in different regions and land covers within the Sentinel-2 image series caused by the unique tide periodic fluctuation phenomenon and elevation trend law in intertidal zones, thereby rendering the method applicable to elevation prediction across the entire spatial range of intertidal zones, rather than being limited to nonvegetated regions. A case study conducted on the muddy intertidal zones of the islands in the Yangtze River Estuary from 2019 to 2023 reveals that the average root mean square errors are 0.33 and 0.69 m for high-mid and mid-low intertidal zones, respectively. The proposed method demonstrates superior performance in terms of vertical accuracy, spatiotemporal resolution, and spatial continuity in comparison to the state-of-the-art waterline detection and inundation frequency methods.