## 4 Discussion
### Visual Analysis and Classification
In some cases, areas of which seagrass vegetation is known from the field surveys, were characterized by diffusely elevated backscatter values, which may be due to elevated structures of the sediment surface induced by the seagrass cover. Comparative areas vegetated by seagrass, however, could be completely inconspicuous in the SAR data. However, the seagrass stocks in the Lower Saxony Wadden Sea are smaller and of significantly lower density than those in the Schleswig-Holstein Wadden Sea. For these reasons, recognition of seagrass was not pursued in this study. Still, seagrass is a parameter required for Wadden Sea monitoring. For test areas in the Schleswig-Holstein Wadden Sea, Ref. (Kal  
marked by residual water, which is an outstanding advantage of this sensor technology. Because of the high temporal availability, SAR data are also predestined to cover periods between sumptuous in situ campaigns, expensive recordings such as lidar scans, or electro-optical acquisitions dependent on daylight and weather. Thus, continuity in the tracking of dynamic structures can be ensured or new events can be discovered in a timely manner by the SAR sensors. Another approach to meet the monitoring requirements is to directly fuse data from different sensor systems to leverage their respective benefits concerning areal coverage, spatial and temporal resolution, sensitivity, and geometric accuracy while also taking into account financial aspects. In this regard, the advances in satellite technology and the open data policy for imagery from an increasing number of sensors, such as recently the sentinel satellites from the ESA Copernicus program, has already promoted the development of image classification methods. Against the background of the different sensor properties, the combination of different SAR sensors as well as SAR and optical sensors has been examined to refine the differentiation between scatterers or to obtain high-resolution multispectral images e.g., [45; 46; 47; 48]. For tidal areas, e.g., Ref. [49] used information from both space-borne microwave (SAR) and optical/shortwave infrared remote sensing to determine sediment grain-size of tidal flats in the Westerschelde, and Ref. [50] investigated the use of multi-frequency SAR data for sediment classification and for the detection of bivalve beds. Results from the DeMarine projects [10; 11; 14] and the WIMO project [15; 17; 20] have also shown that a combination of high spatial resolution SAR data and specific spectral resolution (specific wavelengths) benefits the classification of intertidal habitats. New algorithms and procedures in the area of neural network deep learning [51; 52] may also bring advances in information extraction from satellite data. So, to develop operational methods that harness satellite-based remote sensing data for Wadden Sea monitoring and meet the requirements of monitoring obligations from national and international legislation, the advantages of various sensor classes and methods of information extraction will have to be combined. Regarding the exponential growth of technology and methods of information extraction, interdisciplinary research as well as collaboration of nature protection managers with experts in electro-optical and SAR remote sensing will be absolutely beneficial.