## 3 Coherence Detection and Inland Water Body Monitoring
### Coherence Detection
In the FY-3E GAL reflection signal DDM example, Figure 3d-f, DDMs are dominated by coherent signals, while the rest are dominated by noncoherent signals. In the DDM example for CYGNSS GPS reflection signals, Figure 3a is dominated by coherent signals, while the rest are dominated by noncoherent signals. In the DDM example for TDS-1 GPS reflection signals, all five subplots are dominated by noncoherent signals. Coherent detection plays an important role in high-precision and high-resolution carrier-phase measurement, as carrier-phase measurement can only be performed when reflection is dominated by coherent scattering. In current or future applications of spaceborne GNSS-R, signal coherence is an important condition for high-precision detection. In the application of grazing angle (GA) carrier-phase sea surface height measurement, a centimeter-level height measurement accuracy can be achieved [43]. As early as 2019, [PERSON] and [PERSON] [69; 70] found that coherent GPS reflection signals were often observed in the raw IF signals recorded by CYGNSS. They proposed a closed-loop carrier-phase tracking algorithm based on the Kalman filter for tracking coherent GNSS-R signals and processing these coherent reflection signals through open-loop and closed-loop processing, which is of great significance for achieving a centimeter-level height  
Figure 2: Illustration of coherent and noncoherent GNSS reflection signals [66]. The blue solid line in the figure represents GNSS direct signal, the red solid line represents coherent GNSS reflected signal, and the yellow line represents noncoherent GNSS reflected signal. measurement accuracy. Later, [PERSON] et al. [27] proposed two algorithms for processing low coherent power water reflection signals, namely AHT for low-complexity single-frequency receivers and multi-carrier tracking suitable for wideband front-end hardware. Applying this method to the raw IF signals of CYGNSS can achieve centimeter-level carrier tracking. However, the analysis of tracking data reveals that successful carrier tracking requires a low signal grazing angle, high antenna gain, and low sea surface wind speed. In recent years, some research has been conducted on the coherent detection of GNSS signal reflections. Figure 4 shows the process flows of three commonly used coherent detection methods using spaceborne GNSS-R raw IF signals, namely cycle statistics (circular length and kurtosis), the signal-to-noise ratio method, and the machine learning method (e.g., SVM). The method based on cycle statistics (circular length and kurtosis) determines whether a signal is coherent or noncoherent according to a preset threshold. The signal-to-noise ratio analysis method distinguishes by calculating the signal-to-noise power ratio of a signal. Machine learning classifies through feature engineering selection, model training, and testing. [PERSON] et al. [52] proposed a method for the coherent detection of the carrier phase by analyzing Spire Global CubeSat data. OL carrier-phase tracking was used to obtain 50 Hz GPS L1 and L2 carrier-phase estimation data. Two parameters, circular length (CL) and circular kurtosis [71], were applied for coherence detection and quantification. The results showed that this method has potential in ocean and sea ice monitoring. Meanwhile, the study also indicated that when using filtering methods such as SCANF, semi-coherent reflection signals can achieve almost the same accuracy as coherent reflection in retrieving surface height. However, coherence analysis is limited to the hardware and processing software quality of Spire Global CubeSats. Figure 3: Examples of coherent and noncoherent DDMs observed by different GNSS-R satellites. Each row in the figure represents the DDMs of different GNSS-R satellites; **(a–f)** represent DDM under coherent or incoherent dominance. [PERSON] et al. [74] summarized and compared various GNSS-R coherent detection methods.