## 3 Coherence Detection and Inland Water Body Monitoring
### Coherence Detection
Currently, research on spaceborne GNSS-R coherent detection is mainly based on standard Level 1 DDM products [8; 75; 76; 77; 78; 79] and raw IF signal data. Among them, the first type of detector based on noncoherent integrated real valued DDM power mainly includes the following three types of detectors: detectors using peak amplitude, detectors using waveform shape, and power spread detectors. The second type of complex DDM based on raw IF signal includes the following three main detectors: a coherence gain detector, phase detector, and entropy detector. Each method has different applicable scenarios and requires different algorithms to be selected according to specific applications, making it difficult to evaluate the advantages and disadvantages. In the second type of coherent detector, amplitude and phase information can be used. Table 2 provides a detailed introduction to the three types of detectors based on complex DDMs generated from raw IF signals [74]. Although there are currently many coherent detection methods for spaceborne GNSS-R, these methods struggle to achieve the coherent detection of signals for multiple scenarios and are quantified in different ways. Based on statistical analysis of the signal-to-noise ratio, circular length, and kurtosis distribution, it is determined that the detection problem is not linearly separable [73]. Among these detection methods, the phase difference circular length method has been widely used, but due to the fact that such detection problems are not linearly separable, the selection of a threshold is not reasonable. This may lead to some good signals being erroneously excluded or some poor signals being erroneously judged as coherent. With the rapid development of computer technology, machine learning has provided new ideas for spaceborne GNSS-R coherent detection. [PERSON] et al. [73] proposed a method for spaceborne GNSS-R coherent detection using Support Vector Machine (SVM), which utilizes the OL tracking data of reflected GPS L1 signals received by Spire LEO satellites, selecting a 50 Hz SNR and carrier-phase error as SVM coherent detection features. The results showed that the method has a detection accuracy of 98.66%. The results of this study provide new ideas and methods for further improving coherent detection algorithms. Machine learning can fully utilize the self-learning and adaptive abilities of brain cells to cope with difficult nonlinear situations. In future research, in order to adapt to such nonlinear problems, it may be necessary to use machine learning and deep learning to solve such nonlinear problems and obtain better detection results. Figure 4: Flow charts of signal coherence detection methods [72; 73]. \begin{table}
\begin{tabular}{c c c} \hline
**Detector** & **Introduce** & **Related Formulas** & **Explain** \\ \hline \hline \multirow{7}{*}{Coherence gain detector [74]} & This detector utilizes information from the phase and relative amplitude of the received signal. It distinguishes between coherent and noncoherent signals by comparing the increase in correlated power with a threshold. The longer coherent & \multirow{2}{*}{\(G_{p}=10\log_{10}\frac{T_{1}}{T_{n}}\)} \\  & Integration time \(T_{1}\) and the nominal integration time & \\  & \(T_{1}\) are calculated using the correlated power \(G_{p}\) & \\  & (in dechels). & \\  & In addition, to further reduce noise and compare & \\  & with other coherent detector algorithms, the & \\  & normalized integrated power values need to be & \\  & averaged over a longer period of time, and then these & \\  & average correlated power ratios are normalized by \(T_{1}\), & \\  & which we represent as the quantity “coherence” C. & \\ \hline \multirow{7}{*}{Phase detector [52]} & This detector is based on phase cycle statistics and coherent detection using the carrier phase, using two parameters, circular length (CL) and circular kurtosis, for coherent detection and quantification [71]. & \\  & Circular length: average unit vector length of the dataset.