Multi-case Evaluation of Empirical Methods for Satellite-Derived Bathymetry

Research on the physical, ecological and environmental processes in the coastal zone often rely on the availability of accurate bathymetric information. The traditional method to acquire these data is by conducting depth soundings from a vessel, which is a very accurate way for measuring bottom elevations in all kinds of water, but has as a downside that the acquisition of data is slow. The rate data collection is limited by the vessel speed and swath, which for instance results in months of surveying to accurately map the entire Dutch coastal zone. Therefore, it is mostly applied in targeted surveys where bathymetric information is required for instance in construction project or detailed coastal analysis and modeling. It is not also feasible to conduct an entire survey for such projects and therefore remote sensing data has gained popularity in the past decades. Multiple methods of bathymetry derivation from satellite data has been topic of investigation, One of those is the concept of light attenuation, which was already covered in publications from the 1970s but is still used in many (coastal) researches. A simplification of the complex radiative transfer theory was made by assuming a log-linear relationship between reflected radiance and water depth. This simplification implies that calibration with known elevation data is needed to obtain an accurate bathymetry. These empirical Satellite Derived Bathymetry (SDB) methods were successfully used in multiple researches the past years, but those mostly focused on single locations with typical good SDB conditions (clear water with a high reflective bottom). The main goal of this research was to gain insight in the possible applications and limitations of empirical SDB methods when optical conditions are not as ideal as in previous researches. A multi-case evaluation of these methods was conducted at several different locations on earth with each having particular conditions that affected the reflected radiance ranging from the "most ideal" case with clear water to a case where SDB is hardy possible because of high levels of turbidity.Promising results were obtained in cases where one can be certain that the measured reflectance is coming from the bottom, so in clear waters with hardly any suspended sediment and waves that disturb the path of light to the bottom. The results, with vertical errors below 50 cm, were in accordance to earlier researches. Also in more complex cases, where water clarity was affected by some levels of turbidity, a reliable bottom was still derived, but vertical errors where increasing. The determining factor whether it was possible to use SDB was if measured reflectance was coming from the sea floor. The full potential of a single satellite image was further analyzed by spatial analyses which showed that a small amount of data already gives enough information of calibrate a model to an accurate SDB. However, these results all rely on the initial reflectance values measured by the satellite's sensor These initial values were analyzed more elaborately without the use of calibration data. The concept of the attenuation of light in water was applied to satellite images to assess the bottom derivation without performing any calibration of the algorithm. It was found that, even after correction for atmospheric effect, measured reflectances on images that were captured at different time varied per image, which hindered the temporal analysis. With the addition of ground-truth data, these fluctuations are eliminated by vertically shifting the bottom to the absolute correct level, but when no data is available, differences in measured reflectance does not necessarily be caused by vertical bottom changes. A contribution of these fluctuations was found in the solar zenith angle, which is differs over the year.With SDB it is possible to derive accurate bathymetric maps under good optical condition. When these conditions get more challenging, the uncertainty in the bottom reflection increases, resulting in an increase of uncertainty in SDB as well