A new sea ice concentration (SIC) retrieval algorithm for spaceborne L-band brightness temperature (TB) data

Highlights: What are the main findings? A novel L-band sea ice concentration retrieval algorithm has been developed, which systematically quantifies and constrains four key uncertainties—particularly the Diurnal Amplitude Variation (DAV) signal associated with sea ice freeze–thaw cycles. DAV exhibits the most pronounced effect on the precision of the sea ice concentration retrieval algorithm; constraining all four key uncertainties together achieves a further reduction in RMSE to 7.42%. What are the implications of the main findings? The novel L-band sea ice concentration retrieval algorithm consistently demonstrates high agreement with SSM/I, ship-based SIC data, and SAR SIC, supporting its reliability under various validation scenarios. Integrating the DAV signal into future retrieval models can enhance the understanding of sea ice freeze–thaw processes and improve ice-atmosphere interaction studies in climate modeling and data assimilation. Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions.