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The Greenland ice sheet has had an increasingly negative mass balance during recent decades (Mouginot et al., 2019), and has been responsible for ∼0.76 ± 0.1 mm/yr of global sea level rise (SLR) between 2005(Cazenave et al., 2018. Greenland was the most important cryospheric contributor to SLR during this period, above Mountain glaciers (0.74 ± 0.1 mm/yr) and Antarctica (0.42 ± 0.1 mm/yr) (Cazenave et al., 2018). Mass losses in Greenland are driven by climatic and oceanographic warming (Christoffersen et al.

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From data collected by RV Polarstern, and additional echosoundings provided by national hydrographic offices, research institutions and the InternationalHydrographic Organization (IHO) Digital Bathymetric Data Center, the 1:1,000,000 Bathymetric Chart of the Weddell Sea (AWI BCWS) series has been developed. The heterogeneity of bathymetric data and the lack of observations for ice-covered areas required the incorporation of supplementary geophysical and geographical information. A new semi-automatic procedure was developed for terrain modeling and contouring. In coastal regions, adjacent sub-glacial information was included in order to model the bathymetry of the transition zone along the Antarctic ice edge. Six sheets of the AWI BCWS series in the scale of 1:1,000,000 covering the southern Weddell Sea from 66°S to 78°S and from 68°W to 0°E were recently completed and included in the 1997 GEneral Bathymetric Chart of the Oceans (GEBCO) Digital Atlas CD-ROM. On the basis of these six 1:1,000,000 AWI BCWS sheets, a generalized l:3,000,000-scale bathymetric chart was compiled for the entire southern Weddell Sea. That chart is included in this volume and is described with regard to its significance to other disciplines.

Eelgrass and various macroalgae play important roles in temperate coastal ecosystems, including as habitat for many species, and as a bio-indicator for water quality. However, in turbid or deeper waters, the optical remote sensing methods commonly used for mapping eelgrass do not provide the necessary range for analysis. We are developing a methodology for detecting and characterizing eelgrass and macroalgae beds using water column backscatter data from multi-beam echosounder systems. We are specifically developing methods to map the maximum depth limit, percent cover, functional type (i.e., macroalgae or eelgrass) and canopy height of the beds, because these are difficult to characterize using existing optical and acoustic methods. Water column data was collected using an Odom MB1 sonar in 2014 and 2015 over a variety of vegetated sites selected to represent a range of conditions: dense/sparse eelgrass, long/short eelgrass, mixed macroalgae and eelgrass, eelgrass on muddy or hard substrates, etc. In addition to sonar data, drop camera data was collected, and data from a regional aerial mapping program also exist for comparison. Initial data analysis shows good agreement between drop camera and sonar detections, and patches as small as 1m2 and as short as 20 cm are detectable.