Soil Moisture (SM) is a key geophysical variable that enables a better understanding of the Earth’s hydrological processes. Missions like Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) have been the primary sources of SM estimations for years. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission, planned to launch in 2024, will bring L-band and S-band SAR measurements that will be used for SM estimation. Consequently, investigations to link SMAP with NISAR to produce accurate SM retrievals at improved spatial resolutions will be a focus to many research initiatives. This investigation aims to set the basis to use polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) products to enhance the temporal resolution of NISAR’s L-band SAR data through its typical 12-day period. As a proof of concept, we model SMAP radar backscatter measurements from SMAP-Reflectometry (SMAP-R) measurements, using the 3 months of data collected by the SMAP radar while it was operational. The model is based on the sensitivity of polarimetric GNSS-R to roughness, vegetation, and SM, as well as on the complementary sensitivity existing between forward and backward scatter. Different regression models are implemented using single-, dual-, and full-polarization GNSS-R measurements synthetized from SMAP-R data. This study highlights the importance of the new constellations of polarimetric GNSS-R being built and shows how those frequent measurements can serve L-band SAR missions to improve their time resolution.