# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Soil Moisture
As a continuation of the work in [23], the follow-up investigation in [114] developed an algorithm to retrieve surface soil moisture from GNSS-R observations, focusing in particular on addressing the challenges presented by surface roughness and vegetation effects. The authors attempted to correct for two main factors: the existing vegetation opacity products, which seemed to overestimate the contribution, and the lack of real representation of surface roughness estimations, resulting in retrieval inaccuracies. The authors in [114] proposed a method to correct the surface roughness, together with incident angle dependence and the actual reflectivity value. With this correction, reasonable surface soil moisture values were proven to exist below a 30\({}^{\circ}\) incidence angle. The synergistic use of GNSS-R and L-band microwave radiometry was also explored by authors in [115], who developed the instruments and sent them to space on a CubeSat platform with the intention of providing soil moisture estimations. Four neural network algorithms were tested. The first algorithm used the skin temperature product from ECMWF together with the 16-day averaged NDVI from the moderate resolution imaging spectroradiometer (MODIS) to estimate soil moisture. Their second approach used all inputs from the first model plus L-band radiometry measurements, showing an increased performance with respect to the first model. The authors compared their retrieval with the soil moisture and ocean salinity (SMOS) soil moisture product gridded at 36 km obtaining a 0.074 cm\({}^{3}\)/cm\({}^{3}\) RMSE. The third approach used the GNSS-R data instead, with an error of 0.087 cm\({}^{3}\)/cm\({}^{3}\), as compared in this case to the SMOS 9 km product. Finally, a fourth model combined both radiometer and reflectometer. This outperformed the previous approaches, with an RMSE of 0.063 cm\({}^{3}\)/cm\({}^{3}\), and demonstrated once more the benefits of combining radiometry and GNSS-R data. Several studies have also used reflectometry data from the SMAP radar receiver. This started collecting data in 2015 and currently represents the most extensive space-borne GNSS-R dataset available which is, in addition, spatially and temporally collocated with the SMAP radiometer. Because of the unavailability of a processed calibrated dataset, the data have only been explored in a small set of studies. Initial studies investigated the potential of SMAP reflectometry (SMAP-R) data to identify freeze/thaw state [51] and polarimetric signatures over cryosphere and land [53]. The first assessments in [51,53] did not include direct signal or SMAP antenna filtering corrections. Later on, a complete calibration methodology for the SMAP-R signals was presented [49]. The latest research in SMAP-R was recently published in [52], which presented the formal mathematical description of SMAP-R as the first full polarimetric GNSS-R instrument. The work in [52,54,55] represented the path towards the standardized use of SMAP-R by the scientific community and contributed to the ongoing efforts towards employing GNSS-R for soil moisture estimation. Additionally, the same authors provided a study in [116] of the detection probability of polarimetric GNSS-R signals. This showed the criticality of the selection of the antenna polarizations in future GNSS-R polarimetric mission design.