## 3 Coherence Detection and Inland Water Body Monitoring
### Inland Water Body Detection
However, the GNSS-R instrument carried by CYGNSS satellites can only cover B1C signals, and it is necessary to focus on the combination of different signal components of BeiDou-3 in the future. In 2022, [PERSON] et al. [53] introduced a new method for jointly identifying coherent reflections using the carrier phase and reflection signal strength and applied this method to 50 Hz GNSS reflection measurements on the Spire Global Cubesats and CYGNSS satellites to map inland water bodies. By analyzing the reflection signals on the surfaces of water bodies, they successfully plotted the boundaries and extension ranges of these water bodies. However, GNSS reflection is sensitive to the fraction of surface water and may make it difficult to distinguish water bodies that are close to each other. In 2024, a study by [PERSON] et al. [91] showed that it is feasible to detect inland water bodies under dense biomass using raw IF data and L1 data from the CYGNSS satellite, where the raw IF data is used for signal coherence detection. Their work overcame the challenge of detecting water bodies under dense vegetation cover, providing new ideas for the application of spaceborne GNSS-R technology in complex environments. [PERSON] et al. [42] investigated the effectiveness of using CYGNSS raw IF data for Qinghai Lake boundary detection. By analyzing and processing the CYGNSS raw IF data collected near Qinghai Lake in 2018, higher-delay-resolution and Doppler-resolution DDMs were successfully generated. Compared with the terrain distribution results of Google Earth and global land analysis and discovery, the retrieval results had an error of approximately 0.5 km. This study demonstrates the enormous potential of using spaceborne GNSS-R raw IF data to detect the boundaries of inland water bodies, providing new ideas for monitoring inland water bodies. However, due to the scarcity of data, it is not possible to obtain the complete boundaries of Qinghai Lake. In addition, in a recent study, [PERSON] et al. [93] proposed a method of using CYGNSS raw IF data for multidimensional analysis (including amplitude, time delay, phase, and frequency) to improve water identification ability, in order to address the challenge of limited signal features in distance resolution. This method reduces the distance resolution of water bodies with smaller distances from kilometers to meters, significantly improving the accuracy of inland water detection. [PERSON] and [PERSON] [94] studied the retrieval of river slope based on processing CYGNSS raw IF data. Retrieval was achieved by combining the OL tracking of the reflected signal carrier phase with the application of a new error correction method, demonstrating the potential of using spaceborne GNSS-R to retrieval river slope. Subsequently, [PERSON] and [PERSON] [95] used observation data from the Spire CubeSat to observe the surface gradient of the Mississippi River, confirming that GNSS-R can observe superelevation phenomena, while also observing that L2 signals have higher noise levels under low-ionospheric-gradient conditions. However, if there are steep ionospheric gradients on the signal path, dual-frequency measurements can reduce slope gradient errors. This study provides new insights into the use of GNSS-R phase altimetry for observing relatively narrow rivers. However, compared to previous studies on CYGNSS data, Spire GNSS-R data has higher phase noise. Table 3 summarizes the key content of the literature on the detection of raw IF signals of spaceborne GNSS-R in inland water bodies. The raw IF data of spaceborne GNSS-R has important application value in inland water detection. At present, the raw IF data of spaceborne GNSS-R is scarce and struggles to meet relevant research needs. However, with the continuous accumulation of spaceborne GNSS-R raw IF data and further in-depth research, it will play an important role in the development of future spaceborne GNSS-R missions.