# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.2 CYGNSS Data  
The CYGNSS observations with a SP location that fall into the 9-km grid of any of the ISMN sites throughout 2017 and 2018 are included in the analysis. We used the CYGNSS Level 1 v2.1 Science Data Products to obtain the following observables as CYGNSS-representative inputs to the SM retrieval algorithm: (i) Reflectivity, (ii) SP incidence angle, (iii) TES. The definition and acquisition of each input feature are as follows:  
Reflectivity is the primary CYGNSS deliverable that must be input to the regression since it is the GNSS-R receivers' observation of the changing SM values and surface conditions. The surface reflectivity can be derived from the CYGNSS data products by several methods as described previously in this manuscript and performed by other studies. Four different derivations were investigated in this study: (i) we calculated an approximate reflectivity by substituting the DDM SNR (_ddm_sntr_) into \(P_{RL}^{coh}\) in (4) and calibrating for the instrumental and geometric parameters, as done previously [22, 28]. This calculation is called \(\Gamma_{sntr}\) in this manuscript. (ii) \(\Gamma_{peak}\) is generated similarly to the former approach except that the peak value of the analog power DDM (_power_analog_) instead of DDM SNR is used for \(P_{RL}^{coh}\). For cases where error level in the DDM noise floor is high (DDM SNR and \(\Gamma_{sntr}\) in turn would get erroneous), \(\Gamma_{peak}\) could provide increased correspondence to SM. (iii) _brcs_ is used to calculate the reflectivity as shown in (5), correcting the incoherency assumption by applying the coherent equation as well as compensating the path loss and 4\(\pi\) term; this reflectivity is called \(\Gamma_{brcs}\)[35], \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ISMN Site** & **Location** & **Lat, Lon** & **Land Cover (IGBP)** & **Elev.** & **Slope** \\ \hline
1 - Fort Reno (SCAN) & Oklahoma, US & 35.55, \(-\)98.02 & Croplands & 420 & 1.15 \\
2 - Knox City (SCAN) & Texas, US & 33.45, \(-\)99.87 & Croplands, Grasslands & 446 & 0.98 \\
3 - Perdido Riv Farms (SCAN) & Alabama, US & 31.11, \(-\)87.55 & Crop., Savan., Forests & 94 & 1.21 \\
4 - Prairie View (SCAN) & Texas, US & 30.08, \(-\)95.98 & Grass., Savan., Crop. & 80 & 1.12 \\
5 - Reese Center (SCAN) & Texas, US & 33.62, \(-\)102.04 & Croplands, Grasslands & 1014 & 1.14 \\
6 - Riesel (SCAN) & Texas, US & 31.48, \(-\)96.88 & Grasslands, Croplands & 161 & 1.22 \\
7 - San Angelo (SCAN) & Texas, US & 31.55, \(-\)100.51 & Grasslands & 600 & 0.81 \\
8 - Sellers Lake (SCAN) & Florida, US & 29.10, \(-\)81.63 & Forests, Savannas & 24 & 1.87 \\
9 - Starkville (SCAN) & Mississippi, US & 33.63, \(-\)88.77 & Savan., Forests, Grass. & 75 & 1.46 \\
10 - Uapp Marianian (SCAN) & Arkansas, US & 34.78, \(-\)90.82 & Croplands & 63 & 0.84 \\
11 - Uapp Point Remove (SCAN) & Arkansas, US & 35.21, \(-\)92.92 & Croplands, Grasslands & 99 & 1.28 \\
12 - Uvalde (SCAN) & Texas, US & 29.22, \(-\)99.76 & Grasslands, Croplands & 285 & 1.21 \\
13 - Vance (SCAN) & Mississippi, US & 34.07, \(-\)90.35 & Croplands, Forests & 47 & 0.81 \\
14 - Vernon (SCAN) & Texas, US & 34.02, \(-\)99.25 & Grasslands, Croplands & 373 & 1.28 \\
15 - Willow Wells (SCAN) & New Mexico, US & 33.53 \(-\)103.63 & Grasslands & 1336 & 0.97 \\
16 - Yanco-Y7 (OzNet) & Yanco, Australia & \(-\)34.85, 146.12 & Grasslands, Croplands & 128 & 0.55 \\
17 - Yanco-Y8 (OzNet) & Yanco, Australia & \(-\)34.85, 146.41 & Croplands, Grasslands & 150 & 0.94 \\
18 - Yanco-Y11 (OzNet) & Yanco, Australia & \(-\)35.11, 145.94 & Grasslands, Croplands & 114 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil moisture sites that are analyzed in this study. Elevation (in meters) and slope values are calculated as mean values over the 9-km grid. Figure 3: ISMN site locations with the IGBP land cover classification in the background. (**Left**): SCAN sites from USA, (**Middle**): OzNet sites from Australia, (**Right**): IGBP land cover classification color-code.