The sea ice on the oceans in the Arctic and Antarctic is a relatively thin blanket that significantly influences the exchange between the ocean and the atmosphere. The sea ice thickness is a major parameter, which is of great importance for diagnosis and prediction. Determining seasonal and interannual variations in sea ice thickness was the primary objective of ESA's CryoSat Earth Explorer mission. ESA's second Earth Explorer mission, SMOS, provides L-band brightness temperature data that can also be used to infer the thickness of the sea ice, although that was not its primary objective. Both missions complement each other strongly in terms of spatiotemporal sampling and their sensitivity to different ice thickness regimes. In order to further improve the synergistic use of low-frequency radiometric data for sea ice applications, it is imperative to better characterize the uncertainties and covariances associated with the retrieval. A key factor is a thorough understanding of the physical processes that determine the emissivity of sea ice in order to improve the forward model used for retrieval. A thermodynamic model is used to estimate the vertical temperature profile through the snow and sea ice. Therefore, additional meteorological data such as from atmospheric reanalyses and parameterizations of snow and sea ice properties must be taken into account. Natural sea ice is not a homogeneous medium of uniform sea ice and snow thickness, but can only be described by statistical distribution functions on different spatial scales. Thin ice and open water in leads within the compact pack ice also have a significant influence on the brightness temperature measured by SMOS. In order to take all these effects into account, the forward model or the observation operator must be of the appropriate complexity. The inversion to determine the geophysical sea ice parameters can be optimized with a-priori information and parameterizations as well as with information from other satellite sensors. The presentation will focus on a review of the current retrieval method used to generate the AWI-ESA level 3 and level 4 Sea Ice Thickness products and the way forward to improve the emissivity model and to define a common basis metrics validation to assess algorithms evolution considering that in-situ validation data is only sparsely available.