# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.4 Quality Controls  
The quality control of the data sets plays a significant role in the preprocessing of the data for the SM retrieval. We made use of distinct quality control mechanisms for in situ SM measurements, CYGNSS observables, and ancillary data. The ideal impact of each quality control step is given Table 3, where percent changes in the original dat set is proved as if a particular quality flag is applied alone. In fact, the quality control flags were applied in the left-to-right order in Table 3.  
Invalid SM values from the ISMN measurements (such as negative SM or precipitation value) are filtered out of the reference SM data for both SCAN and OzNet networks. For SCAN sites, SM measurements that correspond to air temperatures below 1 \({}^{\circ}\)C are excluded from the analysis due to the freezing conditions. The OzNet sites do not experience such conditions as the air temperatures in the region are far away from these ranges annually. CYGNSS observations and ancillary data are not collected for the dates that correspond to invalid and freezing-temperature SM data. CYGNSS observations require further care to discard the low quality observations from the training and validation. We investigated two different sets of CYGNSS quality flags as [PERSON] et al. (2020) and [PERSON] et al. (2019) previously applied and observed higher performance with the former. Despite a lack of sufficient investigation on the performances of these two quality flag sets, the weaker performance (ubRMSE = 0.0557) of the latter might be attributed to the reduced number of data samples used in the training (4027 samples after applying flags from ([PERSON] et al., 2019)). The quality flags used \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**ISMN Site**} & \multicolumn{2}{c}{**Initial \#**} & \multicolumn{2}{c}{**Final (\%)**} & \multicolumn{2}{c}{**CYG.**} & \(\theta_{i}\) & \(G_{r}\) & **Peak** & **Water** \\  & **2017** & **2018** & **2017** & **2018** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Fort Reno & 271 & 275 & 69.0 & 66.9 & 5.2 & 2.3 & 30.4 & 11.4 & 53.1 \\ Knox City & 257 & 281 & 58.4 & 56.2 & 6.9 & 2.6 & 27.9 & 9.6 & 37.2 \\ Perdido Riv F. & 248 & 306 & 55.6 & 52.9 & 6.4 & 2.2 & 24.6 & 11.7 & 38.9 \\ Prairie View & 292 & 289 & 31.2 & 31.8 & 7.3 & 0.9 & 24.9 & 9.6 & 4.9 \\ Reese Center & — & 283 & — & 29.0 & 6.4 & 1.4 & 21.9 & 6.7 & 4.24 \\ Riesel & 252 & — & 33.3 & — & 6.8 & 0.4 & 27.8 & 12.7 & 0.0 \\ San Angelo & 249 & 308 & 48.6 & 29.3 & 6.3 & 0.2 & 21.7 & 20.9 & 5.7 \\ Sellers Lake & 246 & 255 & 74.0 & 69.8 & 7.0 & 2.3 & 19.6 & 12.0 & 61.9 \\ Starkville & 207 & 272 & 72.9 & 74.3 & 6.7 & 1.7 & 26.6 & 12.9 & 61.4 \\ Uapb-Marianna & 226 & 267 & 37.2 & 32.2 & 9.1 & 1.1 & 26.8 & 13.1 & 7.9 \\ Uapb-Point R. & 270 & 306 & 73.0 & 75.2 & 5.1 & 1.6 & 28.7 & 9.6 & 64.2 \\ Uvalde & 236 & 298 & 25.4 & 28.9 & 5.7 & 1.4 & 23.7 & 10.0 & 0.0 \\ Vance & 226 & 330 & 41.6 & 40.3 & 7.1 & 2.8 & 29.4 & 19.0 & 13.4 \\ Vernon & 232 & 294 & 33.6 & 28.6 & 5.9 & 1.5 & 26.6 & 9.8 & 1.2 \\ Willow Wells & — & 292 & — & 26.4 & 5.1 & 0.3 & 22.6 & 7.9 & 1.37 \\ Yanco-Y7 & 210 & 287 & 34.8 & 41.8 & 6.2 & 0.9 & 26.7 & 7.7 & 13.5 \\ Yanco-Y8 & 215 & 322 & 40.4 & 37.0 & 7.2 & 2.0 & 26.8 & 8.7 & 13.3 \\ Yanco-Y11 & 235 & 297 & 30.6 & 31.7 & 5.9 & 2.7 & 27.6 & 8.7 & 0.0 \\ \hline Overall & 3872 & 4962 & 47.8 & 43.9 & 6.5 & 1.6 & 25.9 & 11.2 & 22.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The separate, ideal impact of each quality control is shown as percent changes to the original data set: If each quality flag was applied alone to the original data set, the percent changes to the original data set would be as given from the sixth to tenth columns (CYG. : CYGNSS quality flags, \(\theta_{i}\): \(\theta_{i}\) \(<\) 65\({}^{\circ}\), \(G_{r}\): Positive receiver gain, Peak: DDM peak delay bin rows in ([PERSON] et al., 2017; [PERSON] et al., 2017), Water: Removal of inland water bodies).