# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Soil Moisture  
Soil moisture applications have been the focus of many studies. Soil moisture can be obtained from inversing models of the dielectric constant, derived from the measurements, or by directly using GNSS-R observables and soil moisture assessments from other satellites or ground stations and building a geophysical model function, a neural network or some mathematical link that relates them. The authors in [110] developed a method to estimate the dielectric constant using GNSS-R observations. The authors linked the reflectivity computed from the GNSS-R data to the dielectric constant equation used in SMAP algorithms for the SMAP radiometer data. The authors estimated the dielectric constant with an RMSE of approximately 5.73, which was on the order of the SMAP mission's acceptable error. A team from the University Corporation for Atmospheric Research/Colorado University (UCAR/CU) developed a soil moisture product [111] for the CYGNSS mission and made it available in NASA repositories. The dataset contained soil moisture retrievals for the upper 5 cm of the soil surface for the range of latitudes covered by CYGNSS (i.e., \(\pm 38\) degrees latitude). The retrieval used a linear fit between the CYGNSS reflectivity observations and soil moisture retrievals from SMAP mission to later perform downscaling using CYGNSS-only data. The product was validated against 171 in situ soil moisture cal-val sites, with a median ubRMSE of 0.049 cm\({}^{3}\)/cm\({}^{3}\) (std of 0.026 cm\({}^{3}\)/cm\({}^{3}\)) and median correlation coefficient of 0.4 (std of 0.27). The same analysis was performed using SMAP soil moisture for validation, obtaining an ubRMSE of 0.045 cm\({}^{3}\)/cm\({}^{3}\) (std = 0.025 cm\({}^{3}\)/cm\({}^{3}\)) and a median correlation coefficient of 0.69 (std = 0.27). The UCAR/CU soil moisture product was considered as a complementary set of measurements to SMAP product that could provide information at faster revisit times than 3 days. The authors in [112] followed a different approach by employing a machine learning-based algorithm. The algorithm was designed to derive a soil moisture product by simply using CYGNSS's observations. The work employs in situ soil moisture data from the International Soil Moisture Network (ISMN) and other space-borne ancillary data to train the neural network, producing a daily product at 3 km and 9 km. The method accomplished an unbiased RMSE of 0.044 cm\({}^{3}\)/cm\({}^{3}\) with a correlation coefficient of 0.66 as compared to the SMAP soil moisture product obtained at a 9 km resolution. The authors also observedthat the performance degraded for regions of dense forest, high topography, and coastlines. Similar to the approach used in [112], the same authors conducted a deeper analysis of various neural networks schemes over the CONUS [113]. The authors in [23] developed a soil moisture retrieval algorithm using only data from a single pass (e.g., not using multiple passes) at two GNSS frequency bands, L1 and L5, using data from an airborne instrument. The validation of the retrieval was assessed using data from the OzNet soil moisture monitoring network in southern Australia. The study methodology made use of an artificial neural network to retrieve soil moisture, combining reflectivity values and the standard deviation of the reflectivity as a proxy for surface roughness. In addition, the impact of surface roughness and vegetation attenuation on GNSS-R reflectivity measurements was assessed. The work performed in [23] is an example of a methodology that includes multi-satellite data. The GNSS-R retrieval algorithm was trained and compared to a downscaled soil moisture product (20 m resolution); this latter product was derived by combining the SMOS soil moisture with the Sentinel-2 normalized difference vegetation index (NDVI) measurements, and the European Centre of Medium-Range Weather Forecast (ECMWF) Land Surface Temperature.