<p>Soil moisture fulfills a critical role in hydrological processes, climate modeling, and agricultural management. Therefore, obtaining accurate soil moisture distributions with high spatiotemporal resolution has become increasingly essential for understanding land surface dynamics. The data provided by the Cyclone Global Navigation Satellite System (CYGNSS) mission via the use of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technology offer the advantage of high temporal resolution, while synthetic aperture radar (SAR) data can provide information on surface features with high spatial resolution. Thus, these two types of data can be combined to generate soil moisture measurements with high spatial–temporal resolution. In this paper, a new soil moisture retrieval method is proposed by fusing CYGNSS (GNSS-R) and Sentinel-1 (SAR) data to simultaneously achieve high spatial and temporal resolutions. In this method, a functional relationship between the surface reflectivity of the spaceborne GNSS-R sensor and the backscattering coefficient of the SAR is established. By fusing Sentinel-1 and CYGNSS data, a two-layer machine learning framework is constructed for soil moisture retrieval. The machine learning-based model was trained on the basis of Soil Moisture Active Passive (SMAP) products to learn regional soil moisture patterns, and the retrieval independence and accuracy were rigorously validated using in situ measurements from the International Soil Moisture Network (ISMN) over a grassland region in the southern–central United States. The results indicated that the retrieved soil moisture is comparable to the SMAP product, with an average unbiased root mean square error (ubRMSE) of 0.070 cm³/cm³ and an average correlation coefficient of 0.65, but the temporal resolution was significantly enhanced, namely, by 3.9 times on average. This study demonstrates the feasibility of bridging the spatiotemporal gaps of current satellite products through GNSS-R and SAR data fusion.</p>