## 1 Introduction
Furthermore, compared with optical-based data, microwave data usually have a coarse spatial resolution, which limits their potential in some fine-scale applications. In recent years, there have been some studies combining these two kinds of data to retrieve VWC with a higher resolution [19; 20]; however, the huge spatial resolution difference between optical and microwave remote sensing products makes the accuracy and spatial resolution of the fusion results poor in practical applications. Therefore, other superior methods to retrieve VWC are needed. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides us with a new mode to monitor the vegetation information in a long time series. It acts as a relatively new L-band remote sensing technique with relevance for measuring vegetation state using reflected GNSSsignals by recording the interference between a direct GNSS signal and a reflected GNSS signal [21]. Daily VWC information can be acquired in a network named EarthScope Plate Boundary Observatory (PBO) H2O based on GNSS-IR technique [21]. [PERSON] [22] first proposed to use reflected GPS signals to measure sea level from space in 1993. It was subsequently expanded to a variety of ground, aircraft, and space-based platforms for studies of soil moisture [23], ocean winds [24], sea ice [25], ocean tides [26; 27], snow [28], and vegetation. For vegetation, many researchers [29; 30; 31] have used GPS reflections to retrieve vegetation parameters. [PERSON] et al. [32] proposed a method of retrieving VWC from the GPS signal-to-noise ratio (SNR) data. They also showed that there is an approximately linear relationship between the amplitudes of the SNR data and VWC when the water content of vegetation is less than 1 kg/m2. However, when the value exceeds this level, the relationship does not exist, which limits the inversion accuracy. [PERSON] and [PERSON] [21] illustrated that the amplitude of the direct and reflected GNSS interferometric signal is related to the change of VWC, and an index termed the normalized microwave reflection index (NMRI), which is positively related to the change of VWC, was defined. The NMRI is calculated based on the observables of carrier phase and pseudorange on which the soil moisture has a smaller effect than vegetation growth, which can reliably remove the effects of soil moisture. Moreover, the NMRI was validated at four grassland sites in Montana, and the results showed that NMRI is correlated strongly with VWC [33]. Furthermore, compared with other methods of measuring VWC, the NMRI value of vegetation can be obtained each day in the PBO H2O network database, with a higher temporal resolution. The previous studies show that GNSS-IR NMRI data have better potential advantages in detecting the change of VWC than the traditional remote sensing techniques. However, the above mentioned GNSS-IR data in the PBO H2O network database are geodetic-quality ground-site-based observations with a footprint of only 1000 m\({}^{2}\) and the GPS sites are sparsely distributed. Therefore, observations of NMRI cannot be obtained in areas beyond the site footprint or where GPS sites are not set, which restricts the application of NMRI products based on GNSS-IR. Fortunately, the development of point-surface fusion techniques, which can generate data from point scale to surface scale, has provided us with an efficient approach to obtain spatially continuous NMRI map. Therefore, it is required to solve the problem of the wider application of NMRI products based on GNSS-IR. In this study, we propose the idea of fusing site-level NMRI products and optical remote sensing VWC-related indices using machine learning methods to compensate for the spatial limitations of GNSS-IR dataset. By means of correlation analysis, we selected the vegetative and meteorological indices that are highly correlated with the GNSS-IR VWC-related index NMRI.