## 5 Conclusions  
This study innovatively combines ground-based GRSS-IR soil moisture data with surface environmental data. We construct a multi-data fusion soil moisture model based on the GA-BP neural network to generate a soil moisture product of 500 m per day, which improves existing products' temporal and spatial resolution. Experiments were conducted using data from 1 January 2014-1 March 2014, and 10 geenvironmental elements (latitude and longitude information, rainfall, air temperature, four topographic factors (elevation, slope, slope direction, and shading), and NDVI) were input into the model. The correlation coefficient R of the optimal model was obtained as 0.8660, and the ubRMSE was 0.0354. The results showed that the GA-BP neural network could better construct the nonlinear relationship between the geenvironmental elements and soil moisture. Finally, we obtained soil moisture products with a daily spatial resolution of 500 m, compensating for PBO sites' spatial limitation. It also improves the time limit of the existing soil moisture products. We finally obtained maps of the GA-BP inversion soil moisture products with 500 m spatial resolution per day for a total of 15 days during 15 February 2014-1 March 2014. It was analyzed by comparing with the NASA-USDA soil moisture products and rainfall for the same period. The results show that the final generated soil moisture products are more consistent with the NASA-USDA global soil moisture data generated based on microwave remote sensing data. At the same time, the model inversion effect is better in areas with low vegetation cover density and average in areas with high vegetation density or no  
Figure 9: Correlation distribution between GA-BP Inversion soil moisture and NASA–USDA products for site 392.  
vegetation cover. It is also highly consistent with rainfall distribution, and the soil moisture value increases with the increase of rainfall. In conclusion, this paper demonstrates the effectiveness of machine learning methods to obtain high spatial and temporal resolution soil moisture products with great potential in predicting soil moisture through multiple data fusion techniques.  
Our work will focus on expanding the test area to cover the whole US or even the entire world in the future. At the same time, we will explore the difference in NDVI calculation in vegetated and non-vegetated areas to further improve the model's accuracy.  
Conceptualization, [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, all authors; re-sources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project ad-ministration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 42064003).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The GNSS-IR soil moisture used in this study are freely available from the International Soil Moisture Network (ISMN). [[https://ismm.geo.tuwien.ac.at/en/](https://ismm.geo.tuwien.ac.at/en/)]([https://ismm.geo.tuwien.ac.at/en/](https://ismm.geo.tuwien.ac.at/en/)) (accessed on 6 March 2021). All surface environmental data is obtained from Google Earth Engine (GEE). [[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)) (accessed on 9 March 2021).  
The authors declare no conflict of interest.