## 4 Discussion  
### Spatial Relationships Affecting \(\beta\) Values  
We demonstrated the existence of spatial relationships between SMAP/CYGNSS \(\beta\) values and mean annual precipitation, NSSM variability, mean NDVI, and topographic  
Figure 10: A comparison of 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs near New Delhi, India [28.25–29.5N, 75.5–77E]. (**a**) SMAP soil moisture timeseries for a single 9 km grid cell (location denoted by a black star in (**d,e,g,h**) from 1 August 2020 to 30 November 2020. (**b**) Landsat image aggregated from 15 September 2020 to 10 October 2020. This is the only relevant timeframe not obscured by clouds. (**c**) Regional [15–30N, 66–84E] 9 km SMAP TBs averaged from 1 August 2020 to 30 November 2020. The red box shows the area displayed in (**d,e**). (**d**) 9 km SMAP TBs averaged from 1 August 2020 to 30 September 2020. (**e**) 9 km SMAP TBs averaged from 1 October 2020 to 30 November 2020. (**f**) Regional [15–30N, 66–84E] 3 km SMAP/CYGNSS TBs aggregated from 1 August 2020 to 30 November 2020. The red box shows the area displayed in (**g,h**). (**g**) 3 km SMAP/CYGNSS TBs aggregated from 1 August 2020 to 30 September 2020. (**h**) 3 km SMAP/CYGNSS TBs aggregated from 1 October 2020 to 30 November 2020.  
roughness in Section 3.3. However, which of these relationships are most important for determining \(\beta\) values? We synthesize the SMAP/CYGNSS \(\beta\) results below.  
High topographic roughness decreases the correlation between emissivity and reflectivity, regardless of mean annual precipitation, NSSM variability, or mean NDVI. This does not mean that topographic roughness is a good indicator of \(\beta\) values, but rather that the quality of SMAP and CYGNSS observations is reduced in areas with high topographic roughness, due to increased scattering of the microwave signal.  
While NSSM variability is related to mean annual precipitation, NSSM variability is more indicative of \(\beta\) values for two major reasons. First, both low and high mean annual precipitation can create areas with low NSSM variability. Second, NSSM variability can be affected by other factors, including seasonal flooding, irrigation, and the water and energy balance of the surface.  
The two most important factors for determining \(\beta\) values are therefore NSSM variability and mean NDVI. \(\beta\) values generally decrease with increasing NSSM variability (Section 3.3.1). Additionally, \(\beta\) values increase as mean NDVI increases from moderate-to-high values, due to increased vegetation density (Section 3.3.2). NSSM variability is a better predictor of \(\beta\) values for areas with low-to-moderate mean NDVI, and mean NDVI is a better predictor of \(\beta\) values for areas with high mean NDVI.  
The spatial relationships between SMAP/CYGNSS \(\beta\) values and mean annual precipitation, NSSM variability, and mean NDVI are also important when considering the \(\beta\) values produced by different landcover classes. Landcover classes with high mean NDVI (forests) and low mean annual precipitation and NSSM variability (barren areas) tend to produce poorly correlated linear regressions and near-zero \(\beta\) values. Alternatively, landcover classes with low-to-moderate mean annual precipitation, moderate NSSM variability, and moderate mean NDVI (shrublands, savannas, grasslands, and croplands) tend to produce well correlated linear regressions with more negative \(\beta\) values.  
Similar findings are presented in [30]. This study on the relationship between SMAP radar backscatter and SMAP radiometer TB found that linear correlations are best in grasslands and croplands and worst in forests and barren areas. They also found that linear correlations are best for moderate NDVI, with worse correlations for low and high NDVI, and that linear correlations improve as soil moisture variability increases.