# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 4 Results
Keeping in mind that both the science mission requirements for the SMAP mission over its calibration/validation sites were ubRMSE values no higher than 0.04 cm\({}^{3}\)/cm\({}^{3}\)[38] and CYGNSS land application studies were targeted at an ubRMSE of 0.05 cm\({}^{3}\)/cm\({}^{3}\), our algorithm seems capable of generating close values to these levels at least for the current data set and input features of interest. It should be also noted here that, despite not being shown here, a previous version of this analysis was conducted with a subset of the data, input features, and quality controls; the overall performance (overall ubRMSE of 0.0594 cm\({}^{3}\)/cm\({}^{3}\) and Pearson's R of 0.6604) was poorer than the ones reported here, although six of the ISMN sites with almost the best performances of this study were used. These observations are of high importance for the ultimate CYGNSS-based, or GNSS-R based in general, SM retrieval studies since the algorithm presented here has the potential to provide increased estimation performances as new satellite observation data and relevant input features are added to the training. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Input Features** & **RMSE** & **ubRMSE** & **Bias** & **R** \\ \hline (i) \(\Gamma_{smr}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0579 & 0.0579 & 0.0006 & 0.8869 \\ (ii) \(\Gamma_{peak}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0635 & 0.0635 & 0.0006 & 0.8619 \\ (iii) \(\Gamma_{ratio}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0603 & 0.0603 & 0.0007 & 0.8763 \\ (iv) \(\Gamma_{bres}\) + \(\Gamma_{ratio}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0547 & 0.0547 & 0.0010 & 0.9001 \\ (v) \(\Gamma_{bres}\) + TES + NDVI + VWC + Elev. + Slope + h & 0.0549 & 0.0549 & 0.0013 & 0.9001 \\ (vi) \(\Gamma_{bres}\) + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0596 & 0.0596 & 0.0012 & 0.8920 \\ (vii) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + VWC + Elev. + Slope + h & 0.0554 & 0.0554 & 0.0001 & 0.8970 \\ (viii) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + NDVI + VWC + Slope + h & 0.0595 & 0.0595 & 0.0001 & 0.8794 \\ (ix) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + NDVI + VWC + Slope + h & 0.0579 & 0.0579 & 0.0003 & 0.8869 \\ (x) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + h & 0.0564 & 0.0564 & 0.0003 & 0.8972 \\ (xi) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0548 & 0.0548 & 0.0001 & 0.8995 \\ \hline (*) \(\Gamma_{bres}\) + TES + \(\theta_{i}\) + NDVI + VWC + Elev. + Slope + h & 0.0544 & 0.0544 & 0.0002 & 0.9009 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Assessment of the input feature contributions to the learning process. The statistical performance metrics are given for the entire data set. (*) The bottom-most row shows the optimal performance, which in turn has the input features set that is used in this study. for 4/9 of them, and high-performance for another 4/9. It is worth noting that ubRMSE alone would be deceptive as some ISMN sites would have very low trends of SM values in yearly average. However, combined with the correlation performances, SM estimations of our algorithm seem to be in a good agreement with mean SM levels of most of the stations. On the other hand, the model performs poorly on particular stations such that the predicted SM values cannot correlate strongly to the reference SM data. For instance, Starkville, Sellers Lake, and Uapb-Point Remove are such SCAN sites where the model's Pearson's R follow low-to-moderate values for both years. These stations have a common feature that more than 60 percent of the observation data are subject to exclusion from analysis due to the existence of inland water bodies. Therefore, the removal process of the inland water bodies and/or the accuracy of the 4-km grid might require further investigation in the future. San Angelo has another interesting result that Pearson' R for 2017 is much lower than that of 2018.