## 1 Introduction
Therefore, better methods are needed to obtain soil moisture products with high spatial and temporal resolution. The advent of Global Navigation Satellite Systems (GNSS) has provided us with a new paradigm for monitoring long-time series soil moisture information. It uses the same L-band remote sensing technology as the microwave. The difference, however, is that this technique interferes with the direct signal emitted by GNSS with the reflected signal reflected by the ground at the ground receiver. The interference contains changes caused by differences in the ground surface, which monitors the physical parameters of the Earth's surface [16; 17; 18; 19]. In addition, Global Navigation Satellite System-Reflection (GNSS-R) technology and Global Navigation Satellite System-Interferometry (GNSS-IR) technology have been gradually developed based on this technology [20; 21]. Because of its advantages, such as all-weather, all-day, and high spatial and temporal resolution, it has been widely used in the fields of soil moisture, sea surface wind field, sea tide, snow depth, and vegetation change [22; 23; 24; 25; 26; 27]. For soil moisture research, it can be further divided into ground-based GNSS-IR, airborne GNSS-IR, and satellite-based GNSS-R techniques, depending on the location of its GNSS receiver. It has been shown that the effective sensing area of ground-based GNSS-IR reaches at least 120 m\({}^{2}\) and can reach more than 1000 m\({}^{2}\) by combining multiple satellite tracks. It effectively monitors soil moisture from 0 to 5 cm in depth, achieving high accuracy inversion from bare soil to vegetation cover [28; 29]. However, the relatively sparse distribution of GNSS stations eventually leads to the inability to achieve spatial continuity in soil moisture monitoring using ground-based GNSS-IR technology. In terms of airborne GNSS-IR, [PERSON] et al. measured ground soil moisture by airborne GNSS-IR technique; it was jointly analyzed with maps with a high spatial resolution of reflectance, surface temperature, and digital surface models, and experiments showed that topography has an important influence on GNSS-IR signals [30]. [PERSON] et al. combined hyperspectral imagery and airborne GNSS-IR technique inversion of soil moisture; a comparison with Airborne radiometer at L-band (ARIEL) soil moisture estimation was performed to obtain a high-resolution soil moisture product [31]. However, there are flight limitations and a relatively small range due to the airborne GNSS-IR technique. For this reason, the satellite-based GNSS-R technology, which loads GNSS receivers on a small satellite constellation for soil moisture monitoring, was developed [32]. It has become a hot topic of current research because of its advantages of high spatial resolution and low revisit time. [PERSON] et al. developed a relative signal-to-noise ratio (rSNR) for deriving terrestrial soil moisture based on satellite-based GNSS-R; combining the rSNR with soil moisture values from SMAP gives daily soil moisture estimates [33]. [PERSON] et al. also used the reflectance provided by satellite-based GNSS-R, combined with the auxiliary vegetation and roughness provided by the SMAP mission information to give daily soil moisture estimates in a grid with a resolution of 36 km \(\times\) 36 km [34]. In addition to this, a few authors have replaced traditional algorithms with machine learning methods. It also combines a small amount of auxiliary data to improve the spatial and temporal resolution of soil moisture estimation. [PERSON] et al. proposed an algorithm to train a neural network using measured data to invert soil moisture from SMOS observations, and experiments showed that the neural network is an effective nonlinear regression tool [35]. [PERSON] et al.