Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones

Abstract

Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters

“…Remote sensing technology offers an alternative and complementary way to estimate water depth from multispectral or hyperspectral images, which can cover large areas with high spatial resolution and frequent temporal revisit [ 3 , 4 , 5 ]. However, remote sensing of water depth in port areas faces some challenges, such as complex water quality, heterogeneous bottom types, and the influence of human activities [ 6 , 7 , 8 ].…”

Improving the Accuracy of Satellite-Derived Bathymetry Using Multi-Layer Perceptron and Random Forest Regression Methods: A Case Study of Tavşan Island

“…In [13], together with Sentinel-2 data and collected in situ measurements, four different machine learning algorithms, including the RF and MLP models used in this study, were implemented to estimate SDB. While the RF method delivered the most accurate depth values, it also provided the best correlation coefficients at different data acquisition times, reaching a maximum of 0.94, while the MLP method produced 0.85 as the best output.…”