Presented at the 2015 International Geoscience and Remote Sensing SymposiumThe study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef and sea grass habitats. Three statistical methods without considering the physical effects of the input parameters are proposed to calculate the sea surface salinity from SMOS measured TB values and associated auxiliary data. Using these three statistical methods, named as Multiple Linear Regression model, the Principal Component Regression (PCR) model and Quadratic Polynomial Regression (QPR) model, daily and monthly averaged SSS is retrieved with 1° Ã 1° spatial resolution in the South China Sea (in the area of 4°N-25°N, 105°E-125°E) during the period of April to June 2013 for the first time. Results are compared with the corresponding SMOS SSS L3 and Aquarius/SAC-D SSS L3 products and validation is also made using Argo measurements. Validation results show that the root mean square error (RMSE) of QPR model is around 0.46 practical salinity units (psu) compared to 0.58psu for Aquarius/SAC-D daily SSS products. In the mean time, WOA13 SSS data is also used for validation in this area and QPR model gives a 0.54psu value of RMSE and 0.69psu, 0.73psu for SMOS SSS L3, Aquarius/SAC-D SSS products.