Coexistence of swells from distant sources and wind seas generated by local wind field results in complex surface wave condition. Identification and separation analysis of wave components of the wind sea and swell provide a more realistic depiction of the sea state and is important for understanding of the mechanisms of climate variability in the wave field. Spectral separating is one of the most important methods in partitioning waves. Two separating methods including the initial wave steepness method (STPN) and modified wave steepness method (MSTPN) that proposed by the National Data Buoy Center (NDBC) are described in this paper. And the NDBC buoy observations are applied in this study to investigate STPN and MSTPN. Although MSTPN method is improved from the STPN method, it is still not fit for the swell effect. Considering limitations mentioned above, we use spectral energy proportion (SEP) to describe the swell effect and abstract the valid data according to this index. Finally, we give a description of correlation between significant wave height (SWH) and wind speed under the wind sea condition. From results, it is shown that SWH and wind speed of wind sea have a quasiquadratic fitting relationship. In addition, it shows that MSTPN is a better performance in application as expected. Our work for spectral partitioning algorithms could provide a reference for the future work in satellite spectral data. And a practical method for deriving the SWHs from wind speed of scatterometer will be realized on the basis of the empirical wind and sea relationship.