Monitoring and understanding the upper atmosphere processes is important for orbital decay and space physics. Nowadays, Low Earth Orbit (LEO) accelerometers provide a unique opportunity to study thermospheric density variations with unprecedented details. In this paper, thermospheric mass densities variations from Gravity Recovery and Climate Experiment (GRACE) accelerometers are investigated for the period 2003–2016 using the principal component analysis (PCA). The resulting modes are analyzed and parameterized in terms of solar and magnetospheric forcing, local solar time (LST), and annual variations. A better understanding of global thermospheric air density variations is presented, which validates the suitability of our technique and model. The parameterization of the subsolar‐point annual variation shows two maxima around June and only one in December. The LST parameterization shows a new fluctuation controlling a middle latitude four‐wave pattern, with two maxima at 12 h and 21 h LST and two minima at 1 h and 17 h LST. Our parameterizations are suitable to represent small‐scale variations including, e.g., the equatorial mass density anomaly (EMA) and the midnight density maximum (MDM). Finally, the residuals are analyzed in the spectral domain, and additional contributions are found at the frequencies of the radiational tides and at the periods of 83, 93, 152, and 431 days.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.

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