Presented at the 2018 Ocean Salinity Science Team and Salinity Continuity Processing MeetingSalinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Based on the internal consistency of SMOS Sea Surface Salinity retrieved in various locations across swath, a correction was developed to mitigate SMOS systematic errors in the vicinity of continents and seasonally-varying latitudinal systematic errors (Boutin et al. 2018). With the mitigation implemented at the LOCEAN expertise center of SMOS CATDS ('CEC LOCEAN debits v2' products) and in the operational chain of SMOS CATDS ('CPDC L3Q' products), SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico, with r2 between SMOS and SMAP weekly SSS larger than 0.8. Far from land, the standard deviation of the differences between SMOS and SMAP weekly SSS is less than 0.3 pss in most regions of the open ocean, the standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction). Nevertheless, some drawbacks remained. In this presentation, we present updates being implemented in 2018 in the SMOS CATDS processing chains ('CEC LOCEAN debits v3' and operational CPDC products). We optimize the bias mitigation in case of intermittent SSS variability, like close to river mouths, by introducing information about the seasonal variation of the natural SSS variability and by considering non gaussian SSS statistics. We use a more representative error of SMOS SSS by adding information coming from the quality of the retrieval (Chi2 information). While in the previous version, the bias mitigation was limited to 45S-45N, in the new version it is applied over the global ocean. At high latitude, the bias mitigation is now possible owing to a better filtering of RFIs. It also takes into account an empirical dependency of the biases with the sea surface temperature as observed between Argo and Aquarius SSS retrieved using Klein and Swift dielectric constant (Figure 7 of Zhou et al. 2017). In the new products, we provide an estimate of the rain-freshening effect according to Supply et al. 2018 (this workshop). SSS are temporally averaged using a slipping Gaussian kernel with a full width at half maximum of 9 days. New SMOS maps are sampled daily at a spatial resolution 25x25km2; a median filtering over nearest neighbors is applied. With this spatio-temporal resolution and sampling, the characteristics of this newly-processed dataset are very close to the ones of the 8-day SMAP SSS products.