Accurate near-real-time (NRT) estimation of ocean surface currents remains challenging due to sparse in-situ observations and structural model uncertainties. Most operational systems primarily assimilate altimeter-derived geostrophic currents, which omit ageostrophic contributions from wind forcing, coastal processes, and transient mesoscale dynamics. Direct assimilation of satellite-derived ocean surface currents therefore provides a pathway to improve the dynamical consistency of NRT surface current estimates, particularly in regions of highly variable circulation where accurate knowledge of the evolving ocean state is critical for marine operations. We present an end-to-end framework for direct assimilation of high-resolution satellite-derived surface current fields into a Multi-model Ensemble Kalman Filter (MEnKF). Surface currents are retrieved using an adaptive, constrained Maximum Cross-Correlation (MCC) algorithm applied to sequential AVHRR thermal imagery. The Earth Observation (EO)-derived currents are then integrated into a heterogeneous ensemble of global and regional forecasts to explicitly account for structural model uncertainty. Evaluation against coastal HF-Radar observations and regional reanalysis confirms statistically significant improvements over background forecasts. Under optimal observational conditions, the lowest RMSE (0.18 m/s) occurs when 9–12 EO-derived surface current products contribute to each assimilation cycle, accompanied by improved directional consistency relative to reanalysis data. Sensitivity analysis reveals that performance is driven by observational density and spatial representativeness, with maximum skill achieved at intermediate densities of 8<span>–</span>12 images per assimilation cycle. This framework provides a scalable, physically consistent pathway for improving NRT predictions in data-sparse regions.