## 2 Data  
### CYGNSS Data  
The CYGNSS constellation consists of 8 small satellites. Each satellite simultaneously records the reflected signals from four GNSS satellites at 0.5 s intervals, delivering up to 691,200 DDM images every day. Such a massive number of spaceborne measurements provides the foundation for improving the performance of data-driven deep learning algorithms. In this paper, we use the CYGNSS V3.0 L1 scientific datasets for the experimental data. CYGNSS measurements can be divided into two forms: 11 \(\times\) 17 two-dimensional image data (DDM) and the corresponding one-dimensional auxiliary parameter data. DDM primarily includes Raw DDM generated by Delay Doppler Mapping Instrument (DDMI) and Bistatic Radar Cross Section (BRCS) DDM after processing. Raw DDM without any processing is a direct measure of scattered signal power and is commonly used for SSH retrieval. BRCS DDM is the primary parameter for sea surface wind speed retrieval. Since BRCS DDM can represent the sea surface wind speed information more significantly, it has a stronger correlation with the sea surface roughness. In this paper, the Raw DDM and the BRCS DDM are used together as the original input data. The auxiliary parameters utilized in this study are presented in Table 1. The first eight elements are CYGNSS L1 level data products, and the remaining four auxiliary parameters are the main error factors for signal path propagation, including ionospheric delay, tropospheric delay, ERA5 U10, and V10 wind speed. (a) Ionospheric delay: Ionospheric delay will cause range inaccuracies along direct and reflected signal pathways of several meters [19]. The ionospheric delay is estimated using the Global Ionosphere Maps (GIM) of the International GNSS Service (IGS) [31]. The total ionospheric delay can be calculated by the following equation [19]:  
\[\delta_{ion}=(\delta_{ion1}+\delta_{ion2})-\delta_{ion3} \tag{1}\]  
where \(\delta_{ion}\) denotes the total ionospheric delay; \(\delta_{ion1}\) denotes the ionospheric delay between the GPS transmitting satellite and the specular reflection point (SP); \(\delta_{ion2}\) denotes the ionospheric delay between the specular reflection point and the CYGNSS satellite; and \(\delta_{ion3}\) denotes the ionospheric delay between the GPS transmitting satellite and the CYGNSS satellite [32]. (b) Tropospheric delay: The UNB3m model is employed to estimate tropospheric delay [33]. Tropospheric delays are consolidated under 10 km above the ocean surface; therefore, tropospheric correction is only applied to reflected signal paths below the CYGNSS \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Variable Name** & **Brief Description** & **Serial** & **Variable Name** & **Brief Description** \\ \hline
1 & SNR & DDM Signal-to-noise ratio & 7 & SP\_dopp & DDM Specular point & delay column \\ \hline
2 & SP\_incidence & Incidence angle of specular reflection point & 8 & ddm\_noise\_floor & DDM noise floor \\ \hline
3 & rx\_to\_sp\_range & CYGNSS to specular point distance & 9 & Ionosphere & ionospheric delay \\ \hline
4 & tx\_to\_sp\_range & GPS to specular point distance & 10 & Troposphere & tropospheric delay \\ \hline
5 & Peak\_delay & DDM peak delay row & 11 & ERA5 U10 & ERA5 U10 Wind speed \\ \hline
6 & Peak\_dopp & DDM peak Doppler column & 12 & ERA5 V10 & ERA5 V10 Wind speed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief information about auxiliary parameters. spacecraft altitude. The total ionospheric delay can be calculated by the following equation [19]:  
\[\delta_{tro}=(\delta_{tro1}+\delta_{tro2}) \tag{2}\]  
where \(\delta_{tro}\) denotes the total tropospheric delay; \(\delta_{tro1}\) denotes the tropospheric delay between the GPS transmitting satellite and the SP; and \(\delta_{tro2}\) denotes the tropospheric delay between the SP and the CYGNSS satellite;  
(c) Wind speed: Sea surface wind speed is measured using ECMWF's ERA5 reanalysis dataset, which can provide accurate U10 and V10 wind speed data [34].