High-Precision Water Depth Inversion in Nearshore Waters With SAR and Machine Learning

Achieving high-precision, high-resolution monitoring of nearshore water depth is essential for addressing marinedisasters and environmental variations. Synthetic aperture radar(SAR) imaging offers the advantage of all-day, all-weather observations of coastlines, and imaging is unaffected by water quality. The current depth inversion methods typically exhibit an MRE of around 10%, with spatial resolution typically ranging from hundreds of meters to kilometers. However, random forest (RF)can leverage extensive data and complex algorithms to integrate the high resolution of SAR images and the high precision of in situ data into the inversion model. To address this, we have employed ETOPO2022, multibeam bathymetric, and SAR images to create a depth inversion dataset comprising 542 588 data points. To leverage this dataset effectively, we implemented an RF model for depth inversion from satellite images. During the model establishment process, ETOPO2022 data served as the primary training dataset, while high-precision multibeam data compensated for the limitations of low spatial resolution and low accuracy in shallow depths. The inversion model achieved a mean relative error (MRE) of 3.72% and a root mean square error (RMSE) of 2.28 m on an independent dataset. When the model is applied to a larger area, the overall trend of the inversion results is accurate. Compared to reanalysis data, the in version model exhibits higher spatial resolution, approaching20x22 m. It is worth noting that the model demonstrates a strong inversion capability, especially in challenging shallow water areas. When accounting for extraction errors, the model demonstrated a considerable tolerance for errors.