# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 4 Discussion  
The hybrid bio-optical transformation (HBT) methodology was presented for extracting SDB from free-of-charge Sentinel-2A multispectral imagery and based on a minimum number of training chart data. HBT utilizes both empirical approaches and semi-analytical algorithms for calculating the main seawater optical parameters. Sentinel-2A data were initially corrected for geometrical distortions, radiometric errors, atmosphere interaction, sunlight noise, and water column interference. According to [PERSON] et al. (2016) the glint correction of Sentinel-2 data is potentially more challenging than with higher spatial resolution imagery (e.g., WorldView-2) because 10 m pixels may contain a combination of glint at different scales that may be related to surface waves at more or less than 10 m apart. Following this pre-processing, the seawater bio-optical properties were estimated in order to classify the imagery into seabed types of similar optical characteristics. Since there were no previous environmental observations in the region of interest, a semi-analytical model was adopted as the best way to compute IOPs and AOPs. The HBT method does not require previous knowledge of the bottom albedo for the calculation of both the intrinsic and apparent water optical properties; this seems to be an important issue, since no costly and time-consuming field measurements are needed for the collection of the spectral seabed data. Nevertheless, when missing measurements of IOPs, their uncertainties are known to intrude and propagate into their calculation algorithms. The major sources of errors and uncertainties in deriving IOP products are the uncertainties in the \(R_{rs}(\lambda)\) data, due to sensor noise and atmospheric correction error, along with the assumptions and approximations of the IOP parameterization schemes. Another issue for consideration is the absorption coefficient \(\alpha_{w}\) of pure seawater (Equation (8)), for which we used the values provided by [PERSON] and [PERSON] (2007). Most recent laboratory measurements in the ultra-violet and visible wavelengths by exhibit lower values in the UV spectrum, though [PERSON] et al. (2016) derived higher values for seawater in the UV spectrum, possibly due to absorption by dissolved inorganic constituents. These results imply that additional research is required to further bridge the gap among different measuring methods of \(\alpha_{w}\). [PERSON] et al. (2017) summarize the up-to-date approaches for retrieving marine inherent optical properties from ocean color remote sensing. A variety of techniques for potentially improving IOP retrieval exists, depending also on the sensor, computational capacity, and water types. Several IOP optimization schemes have also been implemented; for example, keeping the power parameter Y in the model of [PERSON] et al. as a free variable seems to improve the errors relative to the standard model for most depths, though retrieved IOPs are not necessarily correct. [PERSON] et al. (2016) proposed an adaptive look-up tables (ALUT) inversion approach for the application of semi-analytical models, which facilitates the uncertainty propagation; it was found that, under suitable conditions, a strong relationship between Sentinel-2A SDB and in situ depths exists, especially when combined with mean filtering techniques. Nevertheless, LUT approaches remain computationally expensive, whereas, in the HBT method, limited resources are required. After completing the calculations of the bio-optical parameters, the relationships between the main IOPs and AOPs (\(b_{b}(\lambda)\), \(\alpha(\lambda)\), and \(K_{d}(\lambda)\)) were identified, and were used to classify the area of interest in sub-regions of water with similar bio-optical properties. Five water classes were found in the study area by performing an unsupervised classification based on the spectral response of various seabed and water types.