ICCE 2022

How to Cite

SATELLITE DERIVED BATHYMETRY FOR MONITORING NEARSHORE DYNAMICS. (2023). Coastal Engineering Proceedings, 37, sediment.46.


Research on the physical, ecological, and environmental processes in the coastal zone traditionally rely on the availability of accurate seafloor topography (bathymetry) information. This information becomes increasingly important as coastal environments are under pressure by climate change and anthropogenic developments. Nonetheless, the bathymetry of shallow nearshore waters is only marginally monitored by cost- and time-intensive survey techniques that are dependent on in-field environmental conditions. Here, Satellite Derived Bathymetry (SDB) offers a valuable alternative to enrich available bathymetric data in both data sparse and data rich environments, reducing the need for in-situ measurements in the future. The high spatial and temporal resolution of satellite imagery yields a more comprehensive understanding of our coast and its dynamics. Yet, consistently mapping temporal change is still challenging, as the majority of currently available empirical SDB algorithms are heavily dependent on in-situ data for calibration purposes. In this study we present a methodology for assessing nearshore horizontal morphodynamics using the uncalibrated reflectance signal from satellite sensors while focusing on the Friese Zeegat, a tidal inlet of the Dutch Wadden Sea.


Burgers (2020): Satellite Derived Bathymetry for Times of Absent In-Situ Data. MSc Thesis. Delft University of Technology.

Donchyts, Baart, Winsemius, Gorelick, Kwadijk & Giesen (2016): Earth's surface water change over the past 30 years. Nature Climate Change 6, pp. 810–813.

European Space Agency (2015): User Handbook. ESA Standard Document 64.

Gao (2009). Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Oceanography 33(1), 103-116.

Gorelick, Hancher, Dixon, Ilyushchenko, Thau & Rebecca (2017): Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202.

Hedley, Roelfsema & Phinn (2009): Efficient radiative transfer model inversion for remote sensing applications. Remote Sensing of Environment 113(11), 2527-2532.

Lee, Carder, Mobley, Steward & Patch (1999): Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied optics 38(18), 3831-3843.

Lyzenga (1978): Passive remote sensing techniques for mapping water depth and bottom features. Applied optics 17(3), 379-383.

Sagawa, Yamashita, Okumura & Yamanokuchi (2019): Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images. Remote Sensing 11(10), 1155.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2023 Etiënne Kras, Arjen Luijendijk, Gennadii Donchyts, Giorgio Santinelli, Jaap Langemeijer, Robyn Gwee, Antonio Moreno Rodenas