Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data
- 1Institute of Environmental Physics, University of Bremen, Bremen, Germany
- 2Department of Environmental Meteorology, University of Trier, Trier, Germany
- 3Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
- 4Department of Geography, University of Victoria, Victoria, Canada
- 5Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, Hanover, New Hampshire, USA
- 6B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.