# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 4 Discussion
This classification was determined exclusively according to the relationships of IOPs and AOPs of each seawater class (Table 1), meaning that the accuracy of classification did not depend on the accuracy of IOPs and AOPs. For this reason, the HBT method can be considered more stable in terms of classification capacity compared to other algorithms, since HBT is applied to water classes of homogeneous physical and spectral bottom characteristics. Following classification, the bathymetry in every seabed class and/or water type was retrieved using the best correlation between the logarithm ratios of band reflectance or the logarithm ratios between the intrinsic and the seawater AOPs for each class. Tests revealed that the HBT method can extract SDB by involving a small number of training data points (less than 10 depths in each class), and both training and validation depthswere selected arbitrarily from a nautical chart; this procedure is particularly applicable in areas where insufficient field data exists, or depth soundings remain difficult to collect in situ. Finally, the HBT SDB was compared with the results from the well-known ratio transformation method [22], using the same data set in both the configuration and validation procedures. SDB extracted using the HBT model yielded better results (RMSE = 2.0 m, R\({}^{2}\) = 0.961) against the chart data, regardless of the seabed type and for depths up to 30 m, compared to the ratio transformation method (RMSE = 5.4 m, R\({}^{2}\) = 0.903) at depths between 15 and 20 m. The HBT bathymetric model was executed via the SNAP Desktop, i.e., ESA's software for processing imagery downloaded from the Copernicus hub, and the ratio algorithm is available on the ENVI software. Overall, the HBT advantages can be summarized as the following: (i) The SDB from the HBT model is not a function of any empirical factor, such as the \(n\) parameter of the ratio transform, or \(a_{0}\), \(a_{1}\)... \(a_{4}\) coefficients of a linear transform; it was confirmed that the use of ratios between bands or between their logarithms is a significant tool for bathymetry modelling, since both the HBT model, as well as the ratio transform, exploit the ratios between the spectral bands for extracting SDB from multispectral imagery. (ii) The HBT model may cover water bio-optical parameters with different flora and bottom spectral reflectance. (iii) The bathymetric information extracted requires limited resources to run and can be executed with appropriate free software; there is no requirement for extensive computations or allocation of expensive resources (computing systems, optimization software), nor the collection of costly field data. The use of SDB data is especially useful where vessel traffic or the deployment of aids to navigation indicate that charted data is misleading and there have been no recent surveys to update the charts. Nevertheless, SDB is used by many hydrographic offices as a reconnaissance survey only [28]. Hydrographic offices use SDB data for generating depth curves and not to extract smooth-sheet soundings. This categorizes the SDB data as CATZOC C [29]. However, these limitations may be overcome by the employment of new methods, such as HBT and machine learning, providing more reliable SDB data that will meet the relevant IHO specifications for chart production.