## 3 Coherence Detection and Inland Water Body Monitoring
### Inland Water Body Detection  
Inland water detection refers to the monitoring and analysis of inland water bodies (such as lakes, rivers, and reservoirs), which is of great significance for maintaining the ecological environment, safeguarding human health, and promoting sustainable development. It also plays an important role in carbon cycling and the global climate [87]. Traditional remote sensing technology is commonly used for tracking inland water bodies, such as the Landsat [88] and Sentinel-2 [89] satellites, which can generate high-resolution images to track inland water bodies. However, optical images are limited by clouds and rain, and cannot be observed during severe weather conditions. Although the synthetic aperture radar (SAR) sensor carried by the Sentinel-1 [89] satellite can perform all-weather and all-day ground imaging under various weather conditions, these two types of sensors cannot detect water bodies under vegetation cover and have a long revisit period. The emergence of spaceborne GNSS-R technology provides a new method for detecting inland water bodies, as coherent reflections on land mainly occur on relatively calm surface water bodies. Therefore, GNSS-R technology can be used to detect inland water bodies [53]. Previous studies have shown that phase-based coherence (measured by circular length) is more reliable in indicating the presence of water bodies compared to the signal-to-noise ratio, especially when the water surface dominates [90]. In addition, because the amplitude of coherent signals is usually much higher than that of noncoherent ones, very small water bodies can be detected [91]. In previous studies, the potential of spaceborne GNSS-R for dynamic inland water mapping has been demonstrated [79], as well as its ability to detect water bodies under dense vegetation cover [91]. [PERSON] et al. [79] analyzed the coherence of the Level 1 DDM product of the CYGNSS constellation to detect inland water bodies. This method detects coherence by judging the different \"shapes\" of DDMs in coherent and noncoherent scattering regions. Compared with Pekel water cover from Landsat images, its detection accuracy exceeded 80%. Moreover, this result was based on CYGNSS data records of about 2 years, which is significantly shorter than the multi-year datasets relied on by other inland water covers, demonstrating the great potential of spaceborne GNSS-R for mapping inland water bodies. However, the sampling rate of DDMs is usually 1 Hz or 2 Hz and the spatial coverage along the orbit at 1 Hz is about 7 km, which is challenging for applications that require a high spatial resolution. The raw IF data of spaceborne GNSS-R has a high sampling rate. By setting different integration times, the spatial resolution along the orbit can be significantly improved, providing higher-resolution observations [53]. At the same time, studying the raw IF data of spaceborne GNSS-R is of great significance in the design and optimization of spaceborne GNSS-R instruments. In a recent study, [PERSON] et al. [29] tested the algorithm for detecting surface water in the upcoming HydroGNSS mission by utilizing low-speed DDMs from NASA/CYGNSS and raw IF signals collected from CYGNSS and TDS-1 (with a task accuracy requirement of 90%). In recent years, some researchers have focused on how to use GNSS-R raw IF data to achieve the accurate detection of inland water bodies. [PERSON] et al. [21] demonstrated the application of complex waveforms generated from the CYGNSS FM02 satellite's raw IF dataset for inland water body detection, demonstrating the feasibility and potential of using spaceborne GNSS-R raw IF data in the field of water body detection. In 2021, [PERSON] et al. [92] successfully plotted a flood inundation map using the complex DDM generated after processing the raw data collected by CYGNSS for the first time, demonstrating the ability to use complex DDMs for flood inundation mapping.