A Novel Synergistic Approach Using Altimeter Backscatter for an Improved Radiometer Thin Sea Ice Thickness Retrieval

Abstract. This study proposes a novel altimetry-radiometry synergistic approach for improving SMOS thin sea ice thickness retrievals by incorporating permittivity estimates derived from CryoSat-2 backscatter observations. Since sea ice permittivity remains the dominant source of uncertainty in L-band radiometric thickness retrievals, the sensitivity of the CryoSat-2 backscatter signal to variations in this parameter offers a promising pathway to better constrain it. By integrating these permittivity estimates into a hybrid scheme that includes the inversion of an L-band sea ice emission model using machine learning, the approach aims to enhance the accuracy and robustness of thin sea ice thickness estimates obtained from SMOS. A set of independent in situ datasets is used to validate the proposed methodology and to assess its performance across different ice regimes. The CryoSat-2-derived permittivity values lead to realistic and physically consistent estimates, although its validity is limited to first-year ice. Overall, the synergistic combination of Ku-band altimetry and L-band radiometry yields improved results compared to SMOS-only retrieval methods, which are included as a baseline for reference. This highlights the potential of cross-sensor synergies to advance thin sea ice monitoring, establishing a framework applicable to present and future satellite missions such as CIMR, CRISTAL, and ROSE-L.