# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.3 Ancillary Data
Elevation and slope are used to assess their contribution to SM estimation as proxy parameters for the terrain topography, as topography is known to have impacts on the reflectivity [64]. The use of elevation is aimed at helping the regression model learn the impact of the ellipsoid-based CYGNSS SP calculation on the CYGNSS observations, if possible. The slope, on the other hand, is included in the input features as a coherency/incoherency indicator that could be linked to CYGNSS's TES by ANN. Mean elevation and mean slope for each 4-km grid are generated from the static CGIAR-CSI SRTM 90 m, Version 4 DEM database [50], as similarly done in the literature [35]. DEMs of the ISMN sites can be seen in Figure 5.  
The _h-parameter_ is assumed to be linearly related to the root-mean-square-height surface roughness (7) [38]. Therefore, it is input to the SM retrieval in this study to assess its contribution to account for the surface roughness. _h-parameter_ values are listed in a land cover-based LUT in the SMAP mission, similar to _stem factor_[39]. Therefore, we applied a similar calculation for _h-parameter_ values over the 4-km grids as a sum of LUT _h-parameter_ values that are weighted by the land cover percentages in the scene. Figure 5: Ancillary data visualization for 18 ISMN sites. (**a**) IGBP land cover [IGBP]. The land cover color scale is the same as in Figure 3; (**b**) DEM [m]. DEM color scale is generated separately per each ISMN site; (**c**) inland water bodies [Pekel indices [61]]. The inland water body color scale is the same as in Figure 4.  
Distributions and corresponding statistics of the input features can be seen in Figure 6. These distributions were analyzed before and after the quality controls were applied. However, the distributions and statistics of the data after application of the quality controls did not drastically change. Since the quality controls are not yet described, only the statistics before the quality controls are given. Figure 6 shows that reflectivity follows an almost-perfect Gaussian distribution, so 68% (one-sigma) of the reflectivity values fall in a dynamic range of around 10 dB, whereas 95% (two-sigma) of them are in a dynamic range of roughly 20 dB. This is in parallel to the previously observed and simulated dynamic ranges of around 15 dB over cropland growth seasons [34; 44]. Incidence angle follows a quasi-Gaussian distribution that reflects the variation of the CYGNSS observations, where 95% of the measurements have an incidence angle in a range of approximately \([0,60]^{\circ}\). The TES distribution is mostly around the mean value of \(-13.81\) with few observations through larger negative values, which imply that the trailing edge of the reflectivity waveform from peak has a slope of \(-13.81\) on average. The histogram of the elevation data shows the average elevation values around the ISMN sites. The slope distribution indicates that the regions generally have no topographic relief (majority of data under a terrain slope of 2), with a sufficient variation within a slope range of roughly [0.45, 2]. The h-parameter values show no more surprising evidence than that two peaks occur at the SMAP h-values of 0.108 and 0.156 that correspond to croplands and grasslands, respectively [38], and the rest of the data are distributed in between these values. NDVI data prove a good range of variation, where roughly 95% of the NDVI values fall in an approximate range of [0, 0.58] since data seem to follow a nearly perfect Gaussian distribution. VWC distribution, as derived from NDVI by using _stem factor_ values from the SMAP-based LUT, seems to be a mix of two Gaussian distributions around mean values (approximately 1.5 and 3.5 kg/m\({}^{2}\)). These values likely correspond to grassland and cropland averages, which were the two most common land covers observed in this study. Figure 6: Distributions of the input features (before quality control flags applied). Application of quality controls did not result in large variations.