Conference paper Open Access
In maritime areas where safety of navigation is an essential issue, such as ports, harbours and waterways, hydrographic surveys are regularly performed by traditional echo sounder systems. However, in coastal areas with low
maritime traffic, safety of navigation is usually of minor concern and consequently hydrographic surveys tends to
be performed less frequently. Nevertheless, in these areas, bathymetric data is still needed for other activities such
as coastal monitoring and oceanographic operational forecast models. For these applications Satellite-Derived
Bathymetry (SDB) tends to be accepted as a valid tool for obtaining bathymetric models for shallow waters.
These models can be extracted using several empirical algorithms combining remote sensing imagery with in situ
data. In this study, we intend to evaluate the robustness of bathymetric models obtained for shallow waters in
coastal zones, typically with depths equal or less than 10 m. These models were retrieved from high-resolution
multispectral satellite imagery applying a methodology based on the algorithm presented by Stumpf et al. (2003).
In order to accomplish this purpose, different in situ datasets were used: acquired at different time periods, earlier
or distant dates from the imagery time acquisition, and/or obtained from different sources, such as traditional
hydrographic surveys with echo sounders, Light Detection and Ranging (LiDAR) systems and nautical charts with
ancillary bathymetric data. For each in situ datasets, two different calibration approaches were used, one based on
a linear regression, as proposed by Stumpf et al. (2003), and another applying a quadratic regression. For this study
Sentinel-2A and Landsat-8 imagery covering different locations along the Portuguese coast were used. Based on
all different in situ datasets and calibration approaches, several bathymetric models were obtained, which were
then compared with each other as well as with hydrographic data acquired with echo sounders. The results show
that the proposed SDB methodology delivers bathymetric models with a good level of reliability regardless of
the acquisition time or source of the in situ datasets. Furthermore, the SDB models derived from the quadratic
regression approach appear to be a promising solution for modeling bathymetry for shallow waters. The present
study was developed within the EU H2020 Coastal Waters Research Framework (Co-ReSyF) project and its conclusions will be applied in the validation process of the SDB application available in the Co-ResyF online platform.