# The CYGNSS Mission: On-Going Science Team Investigations
## 2 Methodology, Results, and Discussions
### Land Surfaces
Specifically, a multi-layer ANN model was trained to estimate AGB and tree height using CYGNSS SNR observations which uses training and reference data from the Geocarbon Pan-tropical forest maps by [PERSON] et al. and tree height maps derived by the GeoscienceRecent theoretical developments in GNSS-R and scattering models over rough surfaces were also presented in this session [41]. Results and model validation efforts over the San Luis Valley, CO, showed promising matchups with the CYGNSS L1 Bistatic Radar Cross Section (BRCS). However, these studies also stressed the importance of accurately capturing and modeling multi-scale surface roughness and topographic effects. This session also included several topics about retrieval and characterization of SMC and vegetation properties. A machine learning approach using random forest regression was presented to estimate SMC at a global scale [42]. The model was trained and generated using the reflectivity and relevant geophysical data layers, such as Normalized Difference Vegetation Index (NVDI) and DEM maps, along with SMC from the International Soil Moisture Network (ISMN). Results were validated over the Contiguous United States (CONUS) with consistent spatial patterns and magnitudes as those observed by the SMAP mission. Similarly, by deriving a semi-analytical expression of vegetation transmissivity, and soil reflectivity, it was demonstrated how SMAP and CYGNSS observations can be concurrently leveraged to estimate either the Vegetation Optical Depth (VOD) and the SMC [43] (Figure 7). Additionally, a newly initiated study by [PERSON] et al. sought to examine the effects of assimilation SMAP and CYGNSS SMC in near-surface weather forecasting models. This ongoing study showed that CYGNSS-derived SMC assimilation is on par with SMAP-based forecasts. However, further investigation is required to better quantify the added value by CYGNSS for SMC assimilation. Prior work by [PERSON] et al. [44; 45], however, demonstrates that strongly coupled land-surface assimilation frameworks which assimilate in situ, or SMAP, soil moisture can provide additional short-range and near-surface weather forecasting. The effects of CYGNSS SMC assimilation will be reported in future studies. The use of CYGNSS high resolution \(\sim\)3 km SMC maps was demonstrated by an experimental study supported by SERVIR [46]. This CYGNSS product, along with the \(\sim\)3 km Land Information Systems (LIS) surface model, Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall, and Visible Infrared Imaging Radiometer Suite (VIRRS) vegetation information, was leveraged for locust monitoring applications in East Africa. SERVIR is a joint venture between NASA and the U.S. Agency for International Development. The SERVIR program is collaborating with regional entities in West and East Africa to evaluate the utility of Earth observations (EO), and their contribution to operational desert locust monitoring and tracking systems supported by the United Nations (UN) Food and Agriculture Organization (FAO). Figure 6: The 1\(-\)km land water body mask derived from CYGNSS L1 coherence-detection by [PERSON] et al. The map was generated using 1 year of CYGNSS data. Session 4: Land Processes II  
Several studies have demonstrated the importance of coherent scattering of the GPS reflected signal over land [23; 36; 47; 48; 49]. In general, for coherent returns to exist, the GPS signal has to be reflected over very large flat areas with the surface RMSE height lower than \(\sim\)10 cm at L-Band as presented in [50]. Recent analysis suggests that coherent returns are therefore mostly associated with the presence of inland water bodies [51; 52]. This session will provide updates on current research using the SNR to detect inland water bodies. For land applications, surface roughness is a critical parameter for scattering models as detailed in the session. This surface roughness can be estimated using lidar airborne measurements [53].