Abstract

The use of Satellite-Derived Bathymetry (SDB) constitutes an efficient, cost-effective, time-saving, and scalable approach for generating high-resolution shallow-water bathymetry. In this context, SDB can be proven to be a valuable method for supporting bathymetric surveys of submerged archaeological sites. This paper focuses on the ancient city of Asopos, located on the coast of the modern village of Plytra in Laconia, SE Peloponnese, Greece. The site occupies an extensive coastal area characterised by numerous ancient remains and geomorphological features distributed along the shoreline and within the nearshore zone. To achieve the objective of producing a very-high-resolution SDB map, one empirical and four machine learning approaches were evaluated: (a) the quadratic Stumpf band-ratio model; (b) Categorical Boosting; (c) Random Forest; (d) Extreme Gradient Boosting, with all models trained using the same three optimal spectral band ratios; and (e) the CatBoost model trained using principal components derived from a Principal Component Analysis of the multispectral dataset. These approaches were developed using multispectral WorldView-2 imagery and 208 in situ depth measurements as ground truth, collected across a different range of depths and distances from the coastline. Pan-sharpened imagery was used for visual interpretation. Model accuracy was assessed using an additional validation dataset of 57 depth measurements. This combined approach demonstrates that VHR SDB mapping of the nearshore zone is highly effective when employing three band ratios involving the blue, green, and yellow bands, achieving a root mean squared error of less than 1 m, and can therefore serve as a reliable method for shallow-water geoarchaeological investigations.

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