**5. Conclusions**

Altimeter waveforms are usually contaminated by land, island, sea reef, sea ice, seabed terrain, etc., which leads to incorrect SSHs retracked from these corrupted waveforms [30]. Moreover, the precision of MSSH model established from these poor SSHs is a ffected as well. SSA is a classical technique used in signal processing, and is also sometimes used in denoising signals. Therefore, it can be used to reduce the noise level in altimeter waveforms.

In this paper, the c340-p153 track of Jason-1 from 13.80◦ N to 21.75◦ N was selected as the experimental object to introduce the specific process of SSA to reduce the noise level in altimeter waveforms. All waveforms data of Jason-1 from 2002 to 2013 (including ERM1, ERM2 and GM) were processed by SSA noise reduction to obtain SSA-denoised waveforms, which were retracked by a 50% threshold retracker to obtain corresponding retracked SSHs. Then, these retracked SSHs were used to establish MSSH model over South China Sea with grid of 2 × 2 .

A comparison of the IMP and statistical results of crossover di fferences of the retracked SSHs from the SSA + 50% threshold retracker and those from the 50% threshold retracker showed that the SSA allowed a noise reduction on the Jason-1 altimeter waveforms, and can successfully improve the accuracy of retracked SSHs either in the open ocean or coastal region.

Model 1 and Model 2 were compared with CLS15 and DTU18 in the South China Sea, and the results showed that these four models had a high-degree consistency. Moreover, Model 1 showed higher accuracy than Model 2, and the main di fference between these two models was found mainly in the coastal region. This result indicates that using SSA to reduce the noise level of the Jason-1 altimeter waveforms can e ffectively improve the accuracy of the MSSH model.

The SSA algorithm was used to denoise the waveforms of Jason-1 to establish an MSSH model over the South China Sea. It can also be employed to denoise other satellite altimeter waveforms to establish MSSH models of other regions, even a global MSSH model, which is our main aim for the next study.

**Author Contributions:** Formal analysis, methodology, software, validation, writing—original draft, writing—review & editing: J.Y.; Conceptualization, funding acquisition, investigation, methodology, project administration, supervision: J.G.; Formal analysis, methodology, software, validation: Y.N.; Data curation, resources, visualization: C.Z. and Z.L.; Conceptualization, investigation, methodology, supervision: X.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, gran<sup>t</sup> number 41774001.

**Acknowledgments:** We thank the Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) for providing the Jason-1 sensor geophysical data records (SGDR) products. We also thank the Collecte Localisation Satellites (CLS) and the French Centre National d'Etudes Spatiales (CNES) for providing MSS\_CNES\_CLS15, and the Technical University of Denmark (DTU) for providing DTU18 MSS.

**Conflicts of Interest:** The authors declare no conflict of interest.
