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Monitoring sea ice concentration is required for operational and climate\udstudies in the Arctic Sea. Technologies used so far for estimating sea ice\udconcentration have some limitations, for instance the impact of the\udatmosphere, the physical temperature of ice, and the presence of snow and\udmelting. In the last years, L-band radiometry has been successfully used\udto study some properties of sea ice, remarkably sea ice thickness. However,\udthe potential of satellite L-band observations for obtaining sea ice\udconcentration had not yet been explored.\ud  \udIn this paper, we present preliminary evidence showing that data from the\udSoil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice\udconcentration. Our method, based on a maximum-likelihood estimator (MLE),\udexploits the marked difference in the radiative properties of sea ice and\udseawater. In addition, the brightness temperatures of 100 % sea ice and\ud100 % seawater, as well as their combined values (polarization and\udangular difference), have been shown to be very stable during winter and\udspring, so they are robust to variations in physical temperature and other\udgeophysical parameters. Therefore, we can use just two sets of tie points,\udone for summer and another for winter, for calculating sea ice concentration,\udleading to a more robust estimate.\ud  \udAfter analysing the full year 2014 in the entire Arctic, we have found that\udthe sea ice concentration obtained with our method is well determined as\udcompared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF)\uddataset. However, when thin sea ice is present (ice thickness\ud≲ 0.6 m), the method underestimates the actual sea ice\udconcentration.\ud <br">\udOur results open the way for a systematic exploitation of SMOS data for\udmonitoring sea ice concentration, at least for specific seasons.\udAdditionally, SMOS data can be synergistically combined with data from other\udsensors to monitor pan-Arctic sea ice conditions.</p

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator