###### Abstract  
Accurate prediction of sea surface emission is the key for sea surface salinity retrieval from satellite microwave radiometer. In order to retrieve salinity from satellite observation, several sea surface microwave emission models have been developed based on theoretical or empirical methods and validated by in-situ measurements in different regions. However, their performances are still unclear in the Chinese coastal waters. In this study, based on two cruises measurements in the East China Sea (ECS), including the brightness temperature measured by a shipborne microwave radiometer and other auxiliary data (sea surface salinity, sea surface temperature and wind speed), the performances of different sea surface emission models are tested. The results showed that the developed models provide fairly good accuracy in predicting brightness temperature; for example, the accuracy of small perturbance/small scale approximation model (SPM/SSA), two-scale model (TSM) and empirical model is in the range from 0.6 K to 3 K. Moreover, the TSM and empirical models are further improved by optimizing the model parameters in the ECS. Finally, the sea surface salinity were retrieved from shipborne measured data based on the improved models, and the results show that the root mean square (rms) differences between retrieved and in-situ sea surface salinity is about 0.4 psu, indicating the significant improvement by the regional model parameters. microwave remote sensing; radiometer; sea surface emission 2019 11 2486 2019 11 2486 10.3390/rs11212486 10. (ESTAR) and the scanning low-frequency microwave radiometer (SLFMR), successfully produced \(SSS\) maps in coastal areas in agreement with in-situ measurements with an accuracy of about 1 psu. Based on many experiments, the L-band is evidenced as the optimal frequency for remote sensing of \(SSS\), which has been adopted by SMOS, Aquarius/SAC-D and SMAP. However, the sensitivity of satellite measured brightness temperature to \(SSS\) is quite low. For example, the sensitivity of vertically polarized brightness temperature to \(SSS\) variation is 0.4 to 0.8 K/psu for different observing angles and sea surface temperatures (\(SST\)), and it is only 0.2 to 0.6 K/psu for the horizontal polarization brightness temperature [10]. Thus, remote sensing of \(SSS\) requires a highly accurate retrieval model. It is widely accepted that the corrections of the sea surface and atmospheric effects are essential for remote sensing of \(SSS\), since these effects could alter the value of sensor-measured brightness temperature and introduce errors into the \(SSS\) retrieval process. Besides the atmospheric effects, the increasing of sea surface emissivity due to the sea surface roughness and foam effects is the main source of error, which could significantly hamper the accuracy of \(SSS\) retrieval [11]. Over the past decades, the correction for sea surface roughness effects were studied based on the in-situ and airborne measurements; for example, the experiments made from a tower [12], wind and salinity experiments (WISE) [13; 14], airborne Passive-Active L-band Sensor (PALS) campaign [15] and Combined Airborne Radio instruments for Ocean and Land Studies (CAROLS) campaigns [16; 17]. Many rough surface emission models have also been developed based on the theoretical and empirical methods. Among these models, the small-slope approximation/small perturbation model (SSA/SPM) [18; 19; 20; 21], two scale model (TSM) [22; 23; 24] and empirical/semi-empirical models [25; 26] have been widely used by the research community and implemented in different satellite data processing systems. As the foam effect is significant at high wind speed conditions (above a threshold of 12 m/s) due to strong wave breaking, it has been corrected by numerous models; for example, the semi-empirical models [27; 28] and radiative transfer equation (RTE) based models [29; 30], which were developed to estimate the foam covered sea surface emissivity.