###### Abstract
The above limits severely the backscatter texture content of the recorded seafloor, while angular responses are traditionally analyzed in regard to a full swath strip [9; 17], limiting the spatial density of the extracted statistical parameters. In addition, MBES systems lack in seafloor coverage in shallow waters, as their swath width is proportional (3-4 times) to the depth. Significant technology advances of MBES rest on standardization of the collection and processing of calibrated backscatter data and the introduction of multispectral backscatter [9; 20; 21]. The acoustic signature of seagrass, and in particular _Posidonia oceanica_, is well known, e.g., [22; 23], modulated by the efficiency of photosynthesis [24], i.e., related to the time of day and season at which the seagrass was mapped and how individual responses from algae combine over meadows of different densities [25]. It is also affected by gas content [26] and general health [27], with clear differences between frequencies and imaging angles [28]. Information necessary to reliably identify seagrass, reefs and thin or thick sedimentary layers is limited by the angle of imaging, the spatial resolution and other intrinsic parameters such as beam aperture and pulse length but most importantly by the frequency e.g., [29]. However, the frequency dependent visibility of benthic habitats, including _Posidonia_, is not yet known and experiments on multi-frequency dependencies on benthic habitats is a recent field of evolving research, with recent examples being [30; 31; 32; 33]. As a general rule, the choice of imaging frequency not only directly influences the resolution of the system and the time it takes to map an area, but it is also related to the acoustic response (backscatter intensity and texture) of a seafloor type. Development of multispectral MBES, although highly promising, is still in its infancy and therefore multi-frequency datasets are still mostly acquired by successive seafloor scans of the same area with different transducers, which is generally operationally inefficient or even impossible [21; 33], and/or on using multiple MBES systems on a single survey [20]. On the contrary, the majority of modern SSS have a dual-frequency capacity and so they can offer a multispectral perspective to seafloor mapping [34; 35]. Recently, SSS systems with the capability of simultaneously producing three frequencies have been developed and demonstrated [36]. Additionally, SSS systems offer much higher resolution backscatter images of the seafloor, suitable for detailed habitat mapping through texture analysis [11; 25; 37; 38; 39]. SSS can acquire data from very high grazing angles, not limited to specific beam angles as MBES are. This makes it more suitable for shallow waters. What is considered an important drawback of SSS is its lack of secondary information regarding bathymetry and angular dependency of returning echoes. This way, SSS measurements, as opposed to MBES, cannot be accurately corrected for radiometric and geometric artifacts [40], and it is also hard to investigate angular backscatter dependencies for habitat discrimination. As MBES advances lead to vastly improved maps of benthic habitats, MBES tends to replace SSS for seafloor mapping. Although there is work comparing these two systems [11], there seems to be a research gap concerning if and how SSS and MBES could be used jointly to complement each other in terms of spatial and spectral resolution and mapping coverage, especially in shallow habitats. The present work uses a single frequency MBES and a dual frequency SSS spanning three frequencies (100, 180 and 400 kHz) during a single survey. Texture analysis is applied to each frequency's respective backscatter mosaic, thus extracting a range of textural features from each. In addition, the MBES bathymetry is processed to extract bathymetric features, while well accepted approaches are applied for the extraction of statistics on angular backscatter response curves (ARCs).