Sea ice concentration (SIC) assimilation in operational systems relies predominantly on passive-microwave satellite retrievals, which present well-known limitations, such as smoothing of the ice edge and underestimation of SIC during the melt season. We develop a Random Forest model that bias-corrects AMSR2 SIC observations towards an existing deep learning, SAR-derived product (ML-SAR) in the Arctic. The bias-corrected AMSR2 observations, together with ML-SAR, are then assimilated into the 1/4° configuration of the Met Office Forecast Ocean Assimilation Model (FOAM) as the key experiment. Assimilating ML-SAR and bias-corrected AMSR2 produces a sharper ice edge during the ice growing season and reduces the 5-day forecast Integrated Ice Edge Error by 30 % in summer, relative to a control experiment assimilating only AMSR2 alongside the standard ocean observations. It also largely mitigates the SIC underestimation in the melt season, raising the Critical Success Index against the ice charts in all SIC categories and at all 5-day forecast lead times, by about 25 % on average, with respect to the control. Because the bias-corrected AMSR2 is assimilated wherever ML-SAR is absent, sea ice forecast improvements reach the whole Arctic, including regions of limited SAR coverage, and could not be obtained by assimilating ML-SAR alone. The additional ice volume produced by assimilating both ML-SAR and bias-corrected AMSR2, particularly in spring and summer, contributes to alleviating a known thin bias in the central Arctic in FOAM, but also leads to an overestimation of thickness in the Arctic marginal seas. Overall, the results demonstrate that, by combining the spatial completeness of passive-microwave observations with the fidelity of SAR-based data, this learned bias correction offers a computationally inexpensive, observation-based pathway towards improved Arctic sea ice prediction.