## 4 Discussion
### Visual Analysis and Classification  
Overall, the visual approach proved generic enough to provide an overview of most elements structuring the main research area at Norderney by taking into account not only statistical parameters such as backscatter intensity and contrast, but also shapes, sizes, patterns, and textures of surface features reflected by the SAR data, as well as their spatial distribution and surroundings. Especially patterns, texture, and context information proved to have a great significance for the image interpretation. The importance of contextual information--site and time specific--in SAR image interpretation is also emphasized by Ref. [34] who studied the effects of environmental factors and natural processes on radar backscattering in the Korean tidal areas. Although successful application of TerraSAR-X images is shown, the results also indicate problem zones and variations with the risk of misinterpretation. Areas with no clearly distinctive features or with broad transition zones between habitats may demonstrate the limits of exact demarcation. Mussel beds can exemplify both clearly identifiable areas and problem zones, which will be discussed in the following. Mussel beds are particularly well recognized by their specific internal structures of beds and interspaces, which also allow a certain understanding of their maturity and compactness. Still, due to variability in the appearance of habitats and structures, misinterpretation can occur, e.g., with shell detritus. Mostly, fields of shell detritus are easy to distinguish from mussel beds, because they lack the characteristic internal structures. However, for young mussel beds or very densely covered areas of mussel beds, internal structures may be similar. In these cases, supplementary in situ data is necessary for correct interpretation of the SAR data. Likewise, steeply sloping edges of high sand flats exposed to the sensor could be mistaken for dense beds of mussels or shell detritus, although these are often recognizable from their location, or from comparison with acquisitions of a different recording geometry. In case of doubt, the structure should be clarified on site. Surfaces that have been identified and verified can then be tracked over time in the SAR data with little effort. Or they can also be identified in other places with this acquired knowledge. The sole visual analysis of the TerraSAR-X images may also reach its limits when it comes to determining the exact demarcation of surfaces which directly merge into each other with flowing transitions e.g., where mussel beds are directly surrounded by humpy mud flats or fields of shell detritus that extend far beyond the mussel bed and represent their own habitats. In such cases, again, field observations are needed. Preferably, additional distinguishing characteristics are to be found to design a specific classification method, for example, by exploiting the polarized information of the SAR data. Various authors have shown the additional potential of multi-polarization SAR imagery for the detection of bivalve beds, using fully polarimetric e.g., [35; 36] or dual-copolarized SAR data [37; 38]. [PERSON] et al. (2018) discriminated bivalve beds from the surrounding bare sediments through polarimetric decomposition based on dual-copolarized SAR data. Further research is needed to investigate to what extent polarimetric information can be used for the detection of other surface types in the tidal area. [PERSON] et al. (2018) identified different surface cover types (i.e., seawater, mud flats, and aquaculture algae farms) through polarimetric decomposition, and Ref ([PERSON] et al., 2018), and recently Ref ([PERSON] et al., 2018) pointed to the potential of fully polarimetric interpretation of SAR imagery for classification purposes in tidal areas. [PERSON] et al. (2018) found evidence of mapping characteristics of seagrass beds in SAR data from the Schleswig-Holstein Wadden Sea, whereas in the present study, no general detection of seagrass is proven.