Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition took place between October 2019 and September 2020 giving the rare opportunity to monitor sea-ice properties over a full annual cycle. Here we present 24 high-resolution orthomosaics and 14 photogrammetric digital elevation models of the sea-ice surface around the icebreaker RV Polarstern between March and September 2020. The dataset is based on >34.000 images acquired by a helicopter-borne optical camera system with survey flights covering areas between 1.8 and 96.5 km2 around the vessel. Depending on the flight pattern and altitude of the helicopter, ground resolutions of the orthomosaics range between 0.03 and 0.5 m. By combining the photogrammetric products with contemporaneously acquired airborne laser scanner reflectance measurements selected orthomosaics could be corrected for cloud shadows which facilitates their usage for sea-ice and melt pond classification algorithms. The presented dataset is a valuable data source for the interdisciplinary MOSAiC community building a temporal and spatially resolved baseline to accompany various remote sensing and in situ research projects.

<p>Melt ponds play a key role for the summery energy budget of the Arctic sea-ice surface. Observational data that enable an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in global climate models are spatially and temporally limited.</p><p>Previous studies of shallow water bathymetry of riverbeds and lakes, experimental studies above sea ice and increasing availability of high-resolution aerial sea ice imagery motivated us to investigate the possibilities to derive pond bathymetry from photogrammetric multi-view reconstruction of the summery ice surface topography.</p><p>Based on dedicated flight grids and simple assumptions we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric retrieval methods.</p><p>We present the retrieval algorithm, including requirements to the data recording and survey planning, and a correction method for refraction at the air— pond interface. In addition, we show how the retrieved elevation model synergize with the initial image data to retrieve the water level of each individual pond from the visually determined pond exterior.</p><p>The study points out the great potential to derive geometric and radiometric properties of the sea-ice surface emerging from the increasingly available image data recorded from UAVs or aircraft.</p>