To date, there are eight satellites worldwide carrying various types of microwave scatterometers, providing indispensable support for the remote sensing of global sea surface winds. Quantitative and comprehensive evaluation of the error characteristics of those microwave scatterometer-derived sea surface winds is a prerequisite for the optimal application of multi- scatterometer wind data. This study conducts quality assessments on the sea surface wind data for five Chinese operational scatterometers and two Advanced Scatterometers (ASCAT) from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), using both buoy wind and European Centre for Medium Range Weather Forecasts (ECMWF) data in 2022. The analytical techniques include pairwise comparison, Kolmogorov spectral analysis, and triple collocation analysis. The results of spectral analysis demonstrate that all spaceborne scatterometers are able to depict more spatial variability compared to the ECMWF data, indicating that satellite scatterometers outperform ECMWF in resolving mesoscale dynamic characteristics over sea surface. The triple collocation analysis method not only enables quantitative evaluation of the inherent errors in each data source within the triple collocated datasets, but also facilitates cross-comparison of error characteristics among different scatterometers’ wind products. Overall, the inherent random errors of scatterometer wind u and v omponents range between 0.5-1.3 m/s. The results provide crucial references for the error covariance settings in applications utilizing multi-scatterometer sea surface wind data.