## 5 Conclusions  
Satellite remote sensing of _SSS_ is based on the variation of sea surface emission caused by the change of salinity, but this method generally assumes that the sea surface is perfectly flat. However, in the real world, the sea surface is always rough, and sea surface emission is significantly affected by the sea surface roughness. Thus, one of the most important issues in remote sensing of _SSS_ is to remove the contribution of sea surface roughness on sea surface emission. In this study, two cruise experiments were performed in the ECS to assess and improve the sea surface microwave emission models in the ECS. To obtain the brightness temperature of the sea surface, the contributions of surface reflected downwelling sky radiations including cosmic microwave background radiation and atmospheric emission were first removed by using a developed radiative transfer model. Based on the in situ brightness temperature and auxiliary data (including _SSS_, _SST_, wind speed and direction and _SWH_), the performances of different emission models including TSM, SPM and SSA and empirical models were  
Figure 8: Spatial distribution of measured _SSS_ and errors of retrieved _SSS_ using the different emission models. Interpolations were done by the Ocean Data View software. examined. The results showed that the accuracy of these models varied from 0.6 K to 3 K. The accuracy of TSM was influenced by the choices of parameters in the surface wave spectrum. The error of TSM could reach up to 0.6 K for a0 = 0.006 and decrease to 3 K with the wave spectrum multiplied with a factor 2, and the accuracy of SSA/SPM and empirical models were quite similar, which were 1.1 K and 0.8 K for horizontally and vertically polarized brightness temperatures, respectively. The validation in this study showed that the existing models performed poorly in the ECS and required to be optimized for regional applications. For the TSM, it is well known that the accuracy is affected by the choice of the parameters a0 and cutoff wavenumber \(k_{c}\). It was found that with a0 = 0.006 and \(k_{c}=k_{0}/10\), the brightness temperature could be fairly accurately predicted with an error less than 0.4 K for both polarizations. Furthermore, the empirical models were improved to be applicable for the ECS by optimizing the model parameters, and the brightness temperature variation as a function of both wind speed and _SWH_ was also established. The results showed that the sea surface roughness induced brightness temperature variations were well fitted by wind speed at low incidence angles, especially for horizontal polarization, with the correlation coefficient of 0.88. At large incidence angles, the brightness temperature variations also showed a good correlation with sensitivity and wind speed. However, when all data were used, the accuracy of model prediction became lower, which needs further study to improve the empirical model performance. Based on _SSS_ retrievals with the improved models using shipborne brightness temperature data, it was found that that the modified TSM retrieved _SSS_ with an RMS and standard deviation around 0.4 psu. Compared to the modified TSM model, the accuracy of retrieved _SSS_ using the EMP model was relatively low, which might be caused by the influence of seabed topography on the surface wave spectrum and the coarse resolution of the input _SWH_ data. Data curation, X.J. and F.G.; Formal analysis, X.J. ; Investigation, J.Y. ; Methodology, X.J. ; Project administration, X.H., Y.B. and Q.Z. ; Resources, P.S. ; Supervision, X.H.