# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 4 Results  
Contributions of individual input features and their combinations to the learning process were first assessed in order to determine the optimal input feature set for SM retrieval. Table 4 shows the results of this assessment by using the indices from (i) to (xi) for changing input combinations. The bottom-most line provides the optimal set of input features that is employed in this study. (i), (ii), (iii), and (*) in Table 4 were conducted to compare CYGNSS reflectivity calculations, as shown in the top three rows and the bottom-most row. \(\Gamma_{\textit{brcs}}\) gives the highest correlation and lowest ubRMSE results. (iv) Then, a combination of these reflectivity approximations was assessed with two, three, or four of them being used together. All possible combinations were examined; however, only one of them with the closest performance (\(\Gamma_{\textit{brcs}}+\Gamma_{\textit{ratio}}\)) to the optimal one is given in Table 4. Although there is a slight difference between two, additional \(\Gamma_{\textit{ratio}}\) term reduces the overall performance of the optimal combination so that it is not included in the optimal combination. (v) Instead of giving the SP incidence angle as an input to the system, the reflectivity values are corrected for incidence and fed to the system without angle information; the model performance was slightly worse than feeding SP incidence angles to the learning. Hence, SP incidence angles were chosen into the optimal performance inputs set. (vi) Contribution of TES was examined and found to be significant as its removal increases the ubRMSE and decreases the correlation. TES is also chosen into the input features. (vii) and (viii)  
Figure 7: Flowchart showing the training and validation of the CYGNSS SM retrieval algorithm. were performed to assess the effects of NDVI and VWC to the learning process. Both have a positive impact on the learning performances, but VWC has a more positive contribution compared to NDVI. This is in parallel with the expectation that VWC involves further information of temporal memory in \(NDVI_{min}\) and \(NDVI_{max}\), as well as land cover information through \(stem\_factor\). Both parameters are included into the input features. (ix), (x), (xi) were used to investigate the parameters that are derived from an SRTM DEM data set. All of them appear to positively affect the model, but elevation has the highest and h-parameter has the lowest impact on the overall performance. However, all of them are added to the input features set. After the determination of the set of optimal input features, validation of the method with this set was performed. Figure 8 shows the scatter plots of the ISMN-measured and CYGNSS-retrieved SM values in conjunction with the RMSE, ubRMSE, bias, and Pearson's R values for the entire data set, as well as years 2017 and 2018. In addition, per-site and overall performance statistics of the SM estimation results for the entire data set for both years are shown in Table 5. The entire data set, spanning both years, has a Pearson's R value of 0.9009, which is an indicator of high overall agreement between the CYGNSS-based SM predictions and the reference SM data. Data over either year also show a high correspondence, with Pearson's R values of 0.8532 and 0.9238 for year 2017 and 2018, respectively. These high levels of correlation demonstrate that the presented CYGNSS-based SM retrieval algorithm was successful in capturing the overall trends in approximately 5000 CYGNSS data samples. The ubRMSE values of 0.0575, 0.0520, and 0.0544 cm\({}^{3}\)/cm\({}^{3}\) for 2017, 2018, and entire data set, respectively, are obtained.