## 4 Results and Application
### Application of Coherent Integration Time Optimization Model
#### 4.2.2 Model Application: Extrapolation to Spaceborne Scenario  
Given the reasonable levels of agreement between the simulated model and the airborne experimental data, the proposed coherent integration time optimization model has been implemented to simulate spaceborne iGNSS-R data. Since there is no dedicated iGNSS-R altimetry satellite at present, the results of model optimization can provide a theoretical reference for improving the in-orbit performance of an iGNSS-R-based ocean altimeter in the future. The altimetric precision is mainly determined by the system instrument parameters and observation geometry. These factors will lead to different model optimization results. According to the model's expression, the main parameters that affect the precision are respectively analyzed, including the receiver's orbital altitude, sea surface roughness, elevation angle, and antenna gain. Previously, these parameters were discussed, and this section re-evaluates them in light of the novel model [31; 36]. It needs to be clarified that the Z-V model does not account for the influence of coherent scattering under low wind speed conditions; the minimum wind speed is set to 4 m/s to avoid errors caused by the simulation waveform. The fixed system parameters are summarized in Table 2. The dependence of the precision variation and the optimal coherent integration time on different parameters is analyzed as follows. Figure 8: Effective incoherent average number relative to SP. (1) As shown in Figure 9A, the precision decreases with the altitude of the orbit. When the orbit altitude increases from 200 km to 1000 km, the optimal precision decreases from 0.43 m to 0.94 m. This is because, according to Equation (17), the signal power decreases squarely with the transmission distance, causing the SNR to decrease. On the other hand, the receiver velocity decreases with altitude, resulting in increased waveform correlation and decreased effective incoherent average. As the orbital altitude increases, the optimal coherent integration time increases from 1.5 ms to 3 ms. Higher altitude results in greater energy loss, which calls for a longer time for coherent integration, which improves the SNR and therefore the altimetric sensitivity of the system. \begin{table}
\begin{tabular}{c c} \hline
**Design Parameter** & **Value** \\ \hline Receiver bandwidth & 35 MHz \\ Antenna temperature & 200 K \\ Transmitter altitude & 20,200 km \\ Process method & iGNSS-R \\ Sampling rate & 80 MHz \\ Carrier frequency & 1575.42 MHz (GPS L1) \\ Waveform retracking method & DER \\ Filter bandwidth & 12 MHz \\ Power waveform processing time & 1 s \\ \hline \end{tabular}
\end{table} 
Table 2: The common simulation parameters for spaceborne altimetry scenarios. Figure 9: The simulation variation curves of the estimated altimetric precision with the coherent integration time under different conditions. (**A**) When the wind speed is 6 m/s, the elevation is 60\({}^{\circ}\), the antenna gain is 15 dBi, and the orbit altitude varies. (**B**) When the orbit altitude is 600 km, the elevation is 60\({}^{\circ}\), the antenna gain is 15 dBi, and the wind speed varies. (**C**) When the orbit altitude is 600 km, the wind speed is 6 m/s, the antenna gain is 15 dBi, and the elevation varies. (**D**) When the orbit altitude is 600 km, the wind speed is 6 m/s, the elevation is 60\({}^{\circ}\), and the antenna gain varies. ity. The higher the orbital altitude of the receiver, the more noticeable the improvement in precision of the optimal coherent integration time when compared to the empirical spaceborne coherent integration parameters (1 ms). Therefore, the coherent integration time optimization model is preferred for improving the altimetric precision under the low SNR conditions. (2) As shown in Figure 9B, the precision decreases with wind speed.