Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.