State-of-the-art sea ice models struggle to accurately simulate historical sea ice thickness changes, which could be partially due to inadequate representation of dynamics and thermodynamic processes. High-resolution observations are fundamental tools for improving our understanding of the sea ice physical processes, validating numerical models, and ultimately formulating better sea ice parameterization. Winter observations collected during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in winter 2019-2020 are unique tools for evaluating our models during the freezing season. However, a shortcoming of these observations is that they cannot be easily combined due to differences in measurement techniques and processing chain, impeding a comprehensive characterization of the sea ice system and limiting their diagnostic employment with models. Here, we present an advanced spatiotemporal colocation algorithm designed to integrate airborne measurements collected during multiple helicopter surveys, which provide a two-dimensional characterization of the sea ice surface temperature through infrared camera images and of the surface elevation through an airborne laser scanner at a resolution of approximately one meter over areas spanning several kilometers. The co-located temperature and elevation fields can be combined with boundary layer observations and ground-based transect surveys via drift correction approaches. These observations put in relation for the first time snow freeboard with the equilibrium skin temperature resulting from the surface energy balance while resolving small-scale thickness features (e.g., snow dunes, ridges, and refrozen leads). We will showcase how this innovative observational dataset enables multi-category sea ice model evaluation and the development of new parameterizations.