Satellite‐Derived Bathymetry in Dynamic Coastal Geomorphological Environments Through Machine Learning Algorithms

Abstract

In the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.