Observational data of Arctic Sea Ice Melt Ponds: a Systematic Review of Acquisition and Processing Approaches

Abstract. This review synthesizes current methods for acquiring and processing Earth observation (EO) data relevant to Arctic sea ice melt ponds (MPs), pools of meltwater that form on the ice surface during the polar summer. By reducing albedo, MPs amplify the ice–albedo feedback and alter the sea ice energy budget, exerting a strong influence on the Arctic climate system. Robust observational records are therefore essential for improving sea ice prediction in a rapidly changing and highly sensitive polar environment. Despite this importance, melt pond parameterizations remain underdeveloped in many sea ice models. Advancing these parameterizations, through refinement of existing schemes and integration of novel approaches, is a critical priority for better constraining sea ice evolution and its role in the climate system.

Here we review the main EO methods used in MP studies, including active and passive optical sensors (multispectral and LiDAR) and microwave instruments (synthetic aperture radar, radiometers, and scatterometers). We also summarize melt pond signatures across the electromagnetic spectrum, outlining the strengths and limitations of each sensor. Complementary in situ observations from field campaigns, together with key processing techniques, are discussed, alongside a synthesis of available MP datasets from satellite missions and ground-based campaigns. Persistent EO data gaps, such as cloud cover, limited temporal sampling, and spatial constraints that lead to underrepresentation of different Arctic regions and ice types, remain a major challenge, highlighting the need for future missions with improved resolution, coverage, and spectral capacity.

By compiling and critically assessing these datasets and methods, and identifying current knowledge gaps, this paper provides the most comprehensive review of melt pond observations currently available. It is designed to support refinement of parameterizations and the development of multi-modal modelling approaches, crucial for addressing observational gaps and ultimately advancing the understanding and prediction of Arctic change.