## 1 Introduction
proposed an artificial neural network-based method to retrieve daily soil moisture; soil moisture data from ground-based GNSS-IR and other auxiliary data, including normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness) were input into the model modeling; finally, they obtained daily soil moisture estimates in a 9 km \(\times\) 9 km grid [36]. [PERSON] et al. used neural networks to invert soil moisture using point-surface fusion and combined SMAP and multiple in situ observed soil moisture data based on generalized regression neural networks to build a soil moisture estimation model, ultimately improving the accuracy of the 9 km product of the SMAP task [37]. A follow-up study overcame the scale mismatch problem caused by a small spatial extent based on the triple configuration technique and used neural networks to combine bright temperature data from SMAP and other auxiliary data to build soil moisture estimation models [38]. [PERSON] et al. combined soil moisture data from the Fengyun-3B satellite with surface temperature, normalized difference vegetation index, albedo, digital elevation model based on generalized regression neural networks, longitude, and latitude; finally, they improved the spatial and temporal resolution of the Fengyun-3B satellite from 0.25\({}^{\circ}\) and 2-3 days to 0.05\({}^{\circ}\) and one day [39]. However, most of these studies were based on the Spatio-temporal resolution of existing products, either improving the spatial or temporal resolution of the dataset, relying too much on the Spatio-temporal resolution of existing satellite-based GNSS-R products without comprehensive improvement. Also, the influence of surface environmental elements is not fully considered in using auxiliary data, such as rainfall, altitude, and some other vital factors that are not input. In this paper, we propose a multi-data fusion learning method based on machine learning by combining ground-based GNSS-IR technology soil moisture data and surface environmental data. A multi-data fusion soil moisture model is constructed to obtain a spatially continuous soil moisture product of 500 m per day. We used surface environmental data, including (latitude and longitude information, NDVI, rainfall, air temperature, land cover type, and four topographic factors (elevation, slope, slope direction, and shading)). Since the above surface environmental data and soil moisture are related in a complex non-linear manner, it is difficult to fuse multiple data types and map soil moisture using traditional linear statistical regression algorithms. Compared with traditional algorithms, machine learning techniques excel in dealing with complex non-linear problems. In particular, The Genetic Algorithm Back-Propagation Neural Network model optimized by the genetic algorithm (GA) is highly stable and well fitted. Therefore, we input the processed data into the trained GA-BP neural network model and finally obtained the soil moisture map of 500 m per day for 15 days during 15 February 2014-1 March 2014 for the western coast of the United States. The rest of the paper is described below. Section 2 describes the ground-based GNSS-IR data, the post-validation data National Aeronautics and Space Administration and the U.S. Department of Agriculture (NASA-USDA) products, and the network structure of the GA-BP model. Section 3 describes the study area and the pre-processing process of soil moisture data and related geoenvironmental elements from GNSS-IR stations. Section 4 compares and analyzes the accuracy of the soil moisture products generated based on the GA-BP model and the method's feasibility. Section 5 gives the conclusion of the paper and provides an outlook for future research.