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Accurate water depth estimation is crucial in coastal environmental management, resource exploration, and ecological protection. Traditional water depth measurement methods are often time-consuming and costly, especially in vast sea areas where their application is limited. However, with the rapid development of remote sensing technology, particularly the widespread use of high-resolution satellite imagery, water depth remote sensing has emerged as a more efficient, economical, and widely applicable solution. In this study, we utilized Sentinel-2 satellite data and applied various algorithms to accurately estimate water depth in the Nanshan Port area. The results showed that the Gradient Boosting Machine (GBM) model excelled in monitoring shallow water and coastal environments, effectively addressing challenges such as light attenuation and water scattering in turbid waters. Compared to traditional methods, GBM-generated predictions were smoother and more detailed. This study not only demonstrates the significant potential of satellite remote sensing for water depth measurement but also points to future directions for algorithm optimization and the integration of remote sensing technologies. It is expected to bring revolutionary progress to oceanic scientific research and coastal management. • GBM Excels in Shallow Water Depth Estimation : GBM demonstrates superior performance in shallow waters (0-3m), providing accurate data for coastal environments. • Crucial Data Preprocessing and Control Points: Data preprocessing and control point selection significantly improve accuracy of satellite-derived bathymetry, ensuring reliable results. • Future Directions for Enhanced Accuracy: F uture research should focus on algorithm optimization and hybrid methods, and explore combining SDB with other remote sensing techniques for comprehensive marine information.
Liu et al. (Fri,) studied this question.