Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.
Large-scale inverse Ku-band backscatter modeling of sea ice