Permanent snow and ice cover great portions of the Arctic and the Antarctic. It appears in winter months in northern parts of America, Asia, and Europe. Therefore snow is an important component of the hydrological cycle. Also, it is a main regulator of the seasonal variation of the planetary albedo. This seasonal change in albedo is determined largely by the snow cover. However, the presence of pollutants and the microstructure of snow (e.g., the size and shape of grains, which depend also on temperature and on the age of the snow) are also of importance in the variation of the snow's spectral albedo. The snow's spectral albedo and its bidirectional reflectance are studied theoretically. The albedo also determines the spectral absorptance of snow, which is of importance, e.g., in studies of the heating regime in snow. We investigate the influence of the nonspherical shape of grains and of close-packed effects on snow's reflectance in the visible and the near-infrared regions of the electromagnetic spectrum. The rate of the spectral transition from highly reflective snow in the visible to almost totally absorbing black snow in the infrared is governed largely by the snow's grain sizes and by the load of pollutants. Therefore both the characteristics of snow and its concentration of impurities can be monitored on a global scale by use of spectrometers and radiometers placed on orbiting satellites.

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[1] This study is devoted to the development of a semianalytical algorithm for the determination of the optical thickness, the liquid water path, and the effective size of droplets from spectral measurements of the intensity of light reflected from water clouds with large optical thickness. The probability of photon absorption by droplets in the visible and near-infrared spectral regions is low. This allows us to simplify and modify wellknown asymptotic equations of the radiative transfer theory, taking into account the fact that the single scattering albedo is close to one. Modified asymptotic equations are used to develop the inverse algorithm. We also avoid the use of the Mie theory, applying parametrizations and geometrical optics results with account for wave corrections. The main advantage of the method proposed lays in the fact that the equations derived not only provide a valuable alternative to the numerical radiative transfer solution but also are much more simple than equations of a conventional asymptotic theory. This simplicity allows both the simplification of the cloud retrieval algorithm and, even more important, the insight into various factors involved in a retrieval scheme.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 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 R 2 = 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 R 2 = 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.