# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Vegetation  
Following [49], the same authors performed an analysis in [50] to understand the polarimetric sensitivity of SMAP-R data to crop growth and soil moisture. The investigation considered crop type and height, vegetation water content (VWC), and vegetation optical depth (VOD). It was strategically conducted over the U.S. Corn Belt, where an extensive area is used for agricultural purposes. The standard linear V-to-H polarimetric ratio was used instead of previous definitions [53], showing the potential of SMAP-R to detect these agricultural changes. The authors in [50] showed the difference between the bare soil conditions and peak growth and the maturation conditions of the study area, investigating the role of soil moisture in such assessments. The study concluded that polarimetry GNSS-R, combined with radiometry, is key to solving the uncertainties derived from vegetation and roughness components. This works to improve therefore the final soil moisture estimates and obtaining a vegetation water content or vegetation opacity product. With the work in [52], which provided the full a wide variety of polarimetric assessments can be obtained as it provides the full polarimetric representation of the signals through the Stokes parameters. The first studies using the mathematical formulation in [52] have shown the sensitivity of the SMAP-R signals to land geophysical parameters [54] and the capability to combine SMAP-R with SMAP radiometer to obtain improved soil moisture estimates of densely vegetated areas [55].  
Following the initial study in [117] on VWC estimation using GNSS transmissivity observations, the authors in [118] continued to prove the value of the technique. The study used a GPS receiver to compare the power received in open sky and vegetation conditions. The authors found an increased attenuation due to the vegetation, which was linked to the VWC. The new study in [118] analyzed the attenuation and depolarization produced by the vegetation layer to the GPS signals. The observations were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and NDVI). The highest correlation observed resulted from an interaction between GNSS signal strength and NDVI data, providing a correlation coefficient over 0.85 independently from the elevation angle. Large depolarization effects were also significant at elevation angles above ~50\({}^{\circ}\). Authors in [118] also fitted the data to a zero-order \(\tau\)-\(\omega\) model to estimate the single scattering albedo (\(\omega\)), assuming a fixed VOD (\(\tau\)), to compensate the vegetation scattering effects. The authors concluded that at elevation angles lower than 67\({}^{\circ}\), the \(\omega\) model was not related to optical observations (e.g., NDVI), but to other scattering effects, thereby limiting the range of elevation angles that can be used for soil moisture retrievals. Following studies in [117; 118], a team implemented the same approach [119] by installing the instrument in a robotic dog-shaped platform to assist SMAP mission in calibration and validation activities [119].