Abstract: In this paper, sea surface salinity data of the equatorial Pacific Ocean in 2011~2012, acquired by the Soil Moisture and Ocean Salinity (SMOS) satellite, was processed for quality control and then analyzed for the first time in terms of dynamic process factors that may impact salinity retrieval. The neural network method was introduced to improve the quality-controlled salinity data of the same time period. It was found that precipitation and surface wave induced by precipitation can increase salinity error substantially in a negative trend. Sea surface roughness caused by wind also increased salinity errors. There was a weak positive correlation between wind speed and salinity error. Changes in sea surface temperature had little effect on salinity retrieval. Considering rainfall, wind speed and other major marine dynamic processes, the neural network method was used to revise sea surface salinity data of the equatorial Pacific in December 2011. The results showed that the RMS (root mean square) of salinity reduced from 0.383 7 to 0.244 1. It was found that not only was the salinity error caused by precipitation and other dynamic processes eliminated, but also the high salinity tongue in the equatorial Pacific was revealed, which the Level-3 data failed to.

Key words: soil moisture and ocean salinity satellite, sea surface salinity, neural network, equatorial Pacific