## 1 Introduction  
Satellite altimetry is useful for studying ocean dynamics, environmental factors, and marine gravity fields [1]. The radar altimeters measure the sea surface heights (SSH) along their nadir tracks [2]. However, the spatial resolution is constrained to the period of the satellite; for instance, the period of the exact repeat mission is dozens of days, and the cross-track spacing is larger than 100 km [3]. Different from the radar altimeters which actively transmit signals, the Global Navigation Satellite Systems Reflectometry (GNSS-R) is a bistatic passive microwave radar measurement system [4], that measures the SSHby receiving the signals from GNSS and those signals reflected by the sea surface [5]. Moreover, GNSS-R can simultaneously receive signals from multi-GNSS and provides high spatiotemporal resolution SSH observations [6; 7; 8]. In 1993, [PERSON] used GNSS-R to retrieve sea surface height (SSH) for the first time [9]. This technique has been developed and tested on a variety of platforms, including the ground [10], airborne [11; 12; 13], and spaceborne [14]. The CYGNSS constellation, launched by NASA in December 2016, is the most mature mission to date [15]. CYGNSS consists of eight microsatellites distributed in 510-km orbits, designed to monitor tropical cyclones with high spatiotemporal resolution. To date, a significant number of observations have been obtained. [PERSON] et al. processed the raw datasets from the CYGNSS constellation to evaluate the ocean altimetry precision of spaceborne GNSS-R. With 1-s GPS and Galileo group delay measurements, the results demonstrated that the range precision can reach 3.9 and 2.5 m, respectively [16]. [PERSON] et al. present the implementation of grazing angle carrier phase sea surface altimetry using the signals transmitted from GPS and Galileo constellations and received by CYGNSS. The results showed that the measurement system achieves decimeter-level SSH retrievals, comparable to spaceborne radar altimeters. The observation conditions, however, are relatively strict: the wind speed must be below 6 m/s and the wave height must be below 1.5 m. Under these conditions, the effective data are only 0.04% of the total observations [17]. [PERSON] et al. utilized CYGNSS GNSS-R data to retrieve the SSH in the Indonesian region. In the retrieval process, errors were analyzed and corrected to achieve a retrieval bias of about 6 m [18]. When utilizing spaceborne GNSS-R to retrieve SSH, retracking algorithms, such as HALF, DER, and Z-V models, are typically employed to extract time delay information from the delay waveform (0-Hz Doppler) [19]. However, due to the complex sea surface conditions, these retracking methods may not accurately obtain the time delay of the waveform. Moreover, the received reflected signals by the spaceborne GNSS-R receiver are usually contaminated by background noises because of the high GNSS satellite orbits and low signal transmission power. Deep learning can compensate for the deficiencies of the above retracking methods because it is an efficient approach to process the enormous data obtained by CYGNSS [20]. Deep learning has been adopted extensively in GNSS-R ocean wind speed retrieval [20; 21; 22; 23; 24], soil moisture retrieval [25], and sea ice detection [26] with excellent results. [PERSON] et al. proposed an improved deep learning network framework based on spaceborne GNSS-R data to retrieve global sea surface wind speed; this framework considered auxiliary information including ocean swell, significant wave height (SWH), sea surface rainfall, and wave direction [27]. [PERSON] et al. employed the GALinkNet deep learning method, based on CyGNSS data, to detect inland water bodies; this had better accuracy and effectiveness when compared with other deep networks [28]. [PERSON] et al. employed the XGBoost Machine Learning algorithm to retrieve soil moisture properties from shore-based GNSS-R data [25]. [PERSON] et al. used the CNN algorithm for sea ice detection and density prediction.