Presented at the 2018 Ocean Sciences MeetingA main contribution of satellite Sea Surface Salinity (SSS) is the spatio-temporal monitoring of fresh water plumes at mesoscale. In case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this monitoring was often hampered due to the land-sea contamination of the SMOS interferometric measurement. Kolodziejczyk et al. (2016) developed a methodology to mitigate the SMOS systematic errors in the vicinity of continents, using self-consistency properties of SMOS SSS, that greatly improved the quality of SMOS SSS but the very fresh SSS anomalies remained often overestimated. We revise this methodology by adding a new constraint coming from the SSS natural variability inferred from SMOS measurement and by adding a correction for latitudinal seasonal systematic errors. With this new mitigation, SMOS and SMAP SSS monitor very consistent features in most areas close to continents, and in particular in the Bay of Bengal and in the Gulf of Mexico. Over 20 months and four selected case study, the standard deviation between bi-weekly SMOS and SMAP products is about 0.3pss once outliers are filtered out, rather consistent with the 0.2pss standard deviation of the difference between monthly SMOS SSS and ship SSS in the open ocean. Remaining differences between SMOS and SMAP SSS are due to stronger RFIs pollution in SMOS than SMAP measurement (e.g. Bay of Bengal) and to different spatio-temporal sampling of SMOS and SMAP missions. Comparison with other level 3 SMOS SSS products maintained in near real time show a clear improvement especially in very variable areas close to land.