## 5 Summary and Conclusions  
From the process of developing the empirical FDS GMF for the BF-1 A/B twin satellites, some details, such as the effects of the antenna gain control, elevation angle, inconsistency between satellite channels, and SNR, can be further summarized as follows. The BF-1 A/B DDMs were collected by ranking the antenna gains to the specular points scattered in the antenna footprint. The strongest four antenna gains were selected to be considered closest to the main lobe of the nadir antennas. Without any quality control strategy, the original data had an RMSE above 4 m/s. As Figure 3a illustrates, the percentage of the NBRCS was large at the values between 0 and 20, corresponding to the wind speed range of 5-15 m/s. Consequently, a large portion of the NBRCSs corresponded to this range, so high wind speeds above 15 m/s will be underrated. Therefore, we next explored the following four steps to alleviate this issue. First, a 3-dB half beam-width filter was introduced to ensure the signal is near the main lobe. Thus, the performance of the retrieved wind speed had an RMSE of below 3.8 m/s. Second, the influence of the elevation angle was considered in the GMF by building 63 bins from 28-90\({}^{\circ}\), providing an accuracy of approximately 3.6 m/s of RMSE. Then, the inconsistency between the satellites and channels was considered. As a result, the RMSE became 3.5 m/s. Finally, the most effective factor, SNR, was found to improve the problem of the NBRCS ambiguity between 0 and 20, as illustrated in Figure 3a. After an SNR \(\geq\) 4-dB filter, the percentage of NBRCS ambiguity became 1.59% from 16.86%. Figure 6 shows that the final reanalysis FDS GMF performance has an RMSE of 2.63 m/s at 32.6 m/s, and 2.04 m/s (RMSE) and 0.61 (\(R^{2}\)) against the ASCAT observations, as in Figure 7. Thus, regarding the wind speed accuracy and wind speed range, BF-1 performs better than the existing GNSS-R missions: the RMSE of UK TDS-1 against ASCAT is  
Figure 8: Scatter density plots of GMF-derived wind speeds against NDBC TAO buoy measurements. The colors (from cool to warm) represent the percentage of the maximum number of the densest bin. Results of FDS GMF trained by (**a**) ECWMF reanalysis data and (**b**) ASCAT observation data. 2.21 m/s for winds between 3 and 18 m/s [19], and the RMSE of CYGNSS FDS GMF is ~4 m/s at 20 m/s, compared with the model reanalysis products [23]. The empirical GMFs were also tested with in-situ buoy measurements. The comparison results show a preliminary agreement at the low-to-moderate wind speed range. The RMSEs of the retrieved wind speeds were 1.77 m/s and 1.82 m/s. Figure 8 provides a clear underestimation of the wind speeds. There are two reasons for this: one is the inconsistency of the ECMWF reanalysis wind data, ASCAT observations, and NDBC TAO buoy wind measurements. Meanwhile, the empirical GMFs were not developed based on the buoy measurements, so the behavior is less impressive than the reanalysis and ASCAT results. This paper presents the results of sea surface wind speed retrieval using the first Chinese GNSS-R satellite data collected by BF-1 A/B. After months of in-orbit operation, the data were first referenced with the sea surface wind speeds product of the ECMWF reanalysis and ASCAT observations to train the empirical FDS GMFs. The NBRCS extracted from the collected DDM was used as the observable when developing the GMF. Based on the data filter of the 3-dB half-beam-width antenna and SNR \(\geq\) 4 dB, the empirical GMFs were developed using the power function of the NBRCS (\(\sigma_{0}\)), wind speed (\(U_{10}\)), and elevation angle (\(\theta_{ele}\)). Then, the validation datasets were applied by the empirical GMFs to derive the global sea surface wind speeds to assess the model accuracy. Even without potentially large platform attitude error corrections, the proposed GMF obtained an unbiased RMSE of 2.63 m/s at wind speeds of 32.7 m/s.