## 1 Introduction  
Soil moisture (SM) is an essential parameter in agriculture [1], ecology [2], meteorology [3], and geology [4]. Accurately and timely monitoring SM changes is significant for crop growth assessment, water resource cycle problems, climate and weather forecasting, and geological hazard assessment. Currently, SM monitoring mainly relies on traditional in situ measurements, satellite radar remote sensing monitoring, and model simulation or data assimilation [5; 6; 7]. Traditional in situ measurement methods, such as tensiometry, time-domain reflectometry, and drying-weighing, have a high time resolution. Still, they can only achieve a local point measurement and are difficult to perform monitoring on a regional scale. Satellite radar remote sensing monitoring and assimilation products, such as the Soil Moisture Active Passive (SMAP) or Soil Moisture and Ocean Salinity (SMOS), can provide regional SM [8; 9; 10]. Limited by their temporal and spatial resolution, these remote sensing products cannot easily meet the application requirements of small- and medium-scale fields [11]. Withthe constant development of the Global Navigation Satellite System (GNSS), the use of GNSS multipath signals for the remote sensing of geophysical parameters has increased [12; 13]. It has the advantages of multiple signal sources, wide coverage, and high efficiency, and has been widely used for monitoring SM [14; 15; 16; 17], sea level or tides [18; 19], snow depth or reflector height [20; 21; 22], Flood [23], and vegetation changes [24; 25]. [PERSON] (1993) first achieved ocean height measurements using GNSS reflected signals [26]. On this basis, two new GNSS remote sensing techniques were proposed. One of them is GNSS Reflectometry (GNSS-R), which estimates the characteristic change of geophysical parameters by analyzing the propagation delay or power/waveform information of reflected signals [27; 28; 29; 30; 31]. Subsequently, the technique was further validated for monitoring SM [24; 32; 33], snow depth [34; 35], and wind speed [36]. However, it needs special right-handed circularly polarized (RHCP) and left-handed circularly polarized (LHCP) antennas and requires high-level hardware equipment, which restricts its application and extension to a certain extent. Another technique is GNSS Interferometric Reflectometry (GNSS-IR), which retrieves surface parameters from satellite interference signals received on a single antenna. In addition, GNSS satellite signals are mainly transmitted in L-band, which has less attenuation in the atmosphere and intense penetration [37]. Therefore, with the help of GNSS tracking sites (such as PBO and terrestrial network), it is easier to establish an in situ monitoring system for geophysical parameters. For GNSS-IR monitoring of SM, [PERSON] et al. [38] first proved that there is a certain correlation between the amplitude and phase of reflected signals from a satellite and SM near the surface. [PERSON] et al. [39] further verified that the phase is linearly related to SM by an electrodynamic single-scattering forward model. Due to some differences in SNR data at different elevation angles, [PERSON] et al. [40] fused SNR data within satellite elevation angles of 2-30\({}^{\circ}\) and 30-70\({}^{\circ}\) to improve the time resolution of SM retrieval and the correlation with in situ SM. Considering the impact of vegetation change on SNR, [PERSON] et al. [41] corrected the results considering the impact on vegetation, and the accuracy of SM retrieval was significantly improved. [PERSON] et al. [42] further applied the corresponding SM retrieval algorithm for different surface vegetation, effectively weakening the effect of vegetation on SM retrieval. [PERSON] et al. [43] used a new normalized SNR phase method to retrieve SM affected by vegetation, and the comparison between the retrieved results and the reference values showed good consistency. In addition, [PERSON] et al.