# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Wetlands  
The monitoring of wetlands has also attracted the attention of many researchers. The main reason for this is because there is a gap in characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage linked to wetlands dynamics. The authors in [120] employed GNSS-R signatures from aircraft and satellite measurements to demonstrate that, even under dense vegetation conditions, inundated wetlands can be identified thanks to the coherent scattering of the GPS signal on water, which cannot be measured by optical sensors and monostatic radars. The authors in [121] assessed the synergies of GNSS-R and L-band imaging radar to detect sub-canopy inundation dynamics in tropical wetlands in the Peruvian Amazon. The ALOS2 PALSAR-2 L-band synthetic aperture radar (SAR) and CYGNSS data were merged together with other ground measurements. The study showed that GNSS-R observations were able to detect inundated regions that were undetected by the airborne L-band SAR, suggesting that GNSS-R had a greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Following that work, the authors in [122] then developed an inundation classification algorithm in Tropical wetland complexes by employing GNSS-R data. The study assumed that the GNSS-R signal scattering was coherent in the presence of surface water, producing a strong forward-scattering signature. A multiple decision-tree randomized (MDTR) algorithm was used for wetland and inundation mapping, using ALOS PALSAR-2 as the reference dataset for training and validation. The results obtained in [122] demonstrated the ability of GNSS-R signals to detect inundation under dense vegetation, using a tropical wetland complex located in the Peruvian Amazon. The classification accuracy was at 69% for inundated vegetation regions, 87% for open water regions, and 99% for non-inundated areas. Another approach for wetland monitoring was developed in [123], where authors used CYGNSS data over the Everglades in South Florida to add to previous demonstrations of the CYGNSS capability to obtain a moderate resolution and short revisit time of wetland dynamics in the tropics. In this case, authors used estimates of water depth from the Everglades depth estimation network (EDEN) for inundation mapping. The authors concluded that the strengths of GNSS-R can be used with other techniques to enable high-resolution and high-accuracy studies of wetlands on short timescales. An additional aircraft experiment was conducted in [124] over Caddo Lake in Texas to continue to demonstrate the ability to prove the capability of GNSS-R to detect waterbodies underneath the vegetation canopy. In [124], the airborne-collected GNSS-R data were compared to Sentinel-1 data that had been collected within a week timespan. The authors discussed that the low-altitude measurement allowed for an improved assessment of the GNSS-R detection of the coherent scattering. The results in [124] showed different signal attenuations between open water and canopy-covered water. For example, inundated vegetation, such as the giant Salvinia, resulted in a 2.15 dB difference, and inundated cypress forests showed a 9.4 dB difference, which was 4.25 dB above that observed over dry land. Sentinel-1 data showed a 6 dB difference between the inundated giant Salvinia, and open water, and data were insensitive to standing water beneath the cypress forests. The authors concluded that, at aircraft altitudes, GNSS-R enabled the mapping of inundated regions, even in the presence of dense overlying vegetation.