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Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.

Synthetic aperture radar (SAR) can extract sea surface wind speed information. To extract wind speed information through the geophysical model function (GMF), the corresponding wind direction information must be input. This article introduces some concepts about networked SAR satellites. The networked satellites enable multiple SARs to observe the same sea surface at different incidence angles at the same time. Aiming at the X-band networked SAR data with different incident angles, the cost function is established by using the GMF. By minimizing the cost function, accurate wind speed information can be extracted without inputting wind direction information. When the noise is small, the wind direction information is introduced, and the accuracy of the extracted wind speed will be improved. When the noise is less than 1 dB and the incident angle is greater than 30°, the root-mean-square error (RMSE) of the wind speed extracted by this method is basically less than 2 m/s.