## 3 Coherence Detection and Inland Water Body Monitoring  
### Coherence Detection  
Coherent GNSS-R signals have great potential in achieving high-precision and high-resolution carrier-phase measurements, playing an important role in altimetry, sea-level monitoring, soil moisture monitoring, flood mapping, and other applications [66]. When defining coherent and noncoherent signals, in general, reflection will exhibit a mixture of coherent and noncoherent components. The effective surface roughness is one of the main factors determining the ratio of coherent and noncoherent power in a given reflection, and the Rayleigh parameter is commonly used to describe the surface condition. The Rayleigh parameter is determined by the root mean square surface height \(<\Delta h^{2}>^{\frac{1}{2}}\), wavelength \(\lambda\), and incident angle of reflection \(\theta\)[67], as follows:  
\[R_{a}=2\pi\ <\Delta h^{2}>^{\frac{1}{2}}\ \frac{\cos\theta}{\lambda} \tag{1}\]  
With an increase in roughness, energy is dispersed outside the specular reflection direction. As the Rayleigh parameter increases, it indicates that the power of coherent reflection from that surface area decreases. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Mission**} & \multirow{2}{*}{\begin{tabular}{c} **Type of GNSS-R** \\ **(Receiver Technique)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **Band/Polarization** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GNSS** \\ **System** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Is L0-Level Data** \\ **Publicly** \\ **Available?** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Is L1-Level Data** \\ **Publicly** \\ **Available?** \\ \end{tabular} } \\ \hline \multirow{4}{*}{\({}^{3}\)Cat-2 [55]} & cGNSS-R & \multirow{4}{*}{L1/} & GPS & & \\  & rGNSS-R & & GLONASS & & \\  & iGNSS-R & LHCP, RHCP & Galileo & & \\  & & BeiDou & & & \\ SMAP GNSS-R [56] & cGNSS-R & L2/H, V & GPS & & \\ BuFeng-1 A/B [14] & cGNSS-R & L1/ & GPS & & \\  & cGNSS-R & LHCP & BeiDou & & \\  & cGNSS-R, & L1/ & GPS & & \\  & GNSS-RO & LHCP & Galileo & & \\ FengYun-3E/3F/3G & cGNSS-R, & L1/ & GPS & & \\
[33, 34] & GNSS-RO & LHCP & Galileo & & \\
[33, 34] & GNSS-RO & LHCP & BeiDou & & \\ \({}^{3}\)Cat-5 A/B (FSSCat) & \multirow{2}{*}{cGNSS-R} & L1/ & GPS & & \\
[12] & & LHCP & Galileo & & \\
[3]Cat-4 [60] & cGNSS-R & L1, L2/ & GPS & & \\  & LHCP & Galileo & & \\ PRETTY [13, 61] & iGNSS-R & L1/ & GPS & & \\  & LHCP & Galileo & & \\ TRITON & & GPS & & \\ (FORMOSAT-7R) & cGNSS-R & L1/ & Galileo & & \\
[62, 63] & & LHCP & QZSS & & \\ HydroGNSS [19, 64] & cGNSS-R & L1, E1/ & GPS & & \\  & LHCP, RHCP & Galileo & & & \\  & & GPS & & & \\  & cGNSS-R, & L1, B1, E1/ & GLONASS & & \\ Tianmu-1 [65] & cGNSS-RO & LHCP, LHCP + & Galileo & & \\  & RHCP, H + V & BeiDou & & \\  & & QZSS & & \\ MuSat & & & & & \\ Constellation [18] & – & – & – & – & – \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 2 shows the difference between coherent and noncoherent GNSS signal reflections. As shown in the figure, coherent reflection occurs when the reflection surface is sufficiently smooth, while noncoherent reflection occurs when the roughness \(\varepsilon\) of the reflection surface is equal to or greater than the signal wavelength \(\lambda\). The Rayleigh criterion provides rough guidance for distinguishing between smooth and rough surfaces with specific wavelengths and elevation angles \(\theta\) of reflected signals [68], as follows:  
\[\varepsilon<\frac{\lambda}{8\sin(\theta)} \tag{2}\]  
Figure 3 shows examples of coherent and noncoherent DDMs observed by different GNSS-R satellites. As shown in the figure, in the FY-3E BDS reflection signal DDM example, Figure 3e,f, DDMs are dominated by coherent signals, while the rest are dominated by noncoherent signals. In the FY-3E GPS reflection signal DDM example, all five subgraphs are dominated by noncoherent signals for DDMs.