Satellite-Derived Bathymetry Using a Fast Feature Cascade Learning Model in Turbid Coastal Waters

Abstract

Obtaining accurate bathymetric maps is very valuable for marine environment monitoring, port planning, and so on. Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency, but it is limited by the quantity, quality, and water quality of samples. This study introduces a fast feature cascade learning model (FFCLM) to enhance the accuracy of bathymetric inversion from multispectral satellite images, particularly when limited field samples are available. FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting. Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth, satellite, and in situ data. Comparative analysis with conventional machine learning algorithms, including support vector machine, random forest, and gradient boosting trees, indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas. This is especially more pronounced when using small training samples (n < 100). Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion. This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters, utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.