## 4 Discussion  
To better understand the spatiotemporal behavior, the evaluation of the S1A soil moisture should be as thorough as possible. However, with only one soil moisture observation station in the study site, the evaluation using the in situ measurements is limited in the spatial dimension. Therefore, comparisons with four satellite soil moisture products were adopted in this study to further evaluate the S1A soil \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SM\_EOF1** & **SM\_EOF2** & **SM\_EOF3** & **SM\_EOF4** \\ \hline Clay & \(0.48\)\({}^{\star}\) & \(-0.08\) & \(-0.09\) & \(-0.03\) \\ Sand & \(-0.49\)\({}^{\star}\) & \(0.10\) & \(0.10\) & \(0.03\) \\ Elevation & \(0.08\) & \(-0.10\) & \(-0.01\) & \(0.03\) \\ NDVI\_EOF1 & \(-0.40\)\({}^{\star}\) & \(-0.10\) & \(0.23\)\({}^{\star}\) & \(0.10\) \\ NDVI\_EOF2 & \(-0.42\)\({}^{\star}\) & \(-0.02\) & \(0.10\) & \(0.10\) \\ Temp\_EOF1 & \(0.28\)\({}^{\star}\) & \(-0.11\) & \(-0.16\)\({}^{\star}\) & \(0.02\) \\ Temp\_EOF2 & \(-0.34\)\({}^{\star}\) & \(0.18\)\({}^{\star}\) & \(0.16\)\({}^{\star}\) & \(0.01\) \\ Temp\_EOF3 & \(0.04\) & \(-0.19\)\({}^{\star}\) & \(0.08\) & \(0.01\) \\ \hline \hline \end{tabular} \({}^{\star}\) Significant at \(p<0.05\).  
\end{table} 
Table 3: [PERSON]’s correlation coefficients (\(R\))s between the empirical orthogonal functions (EOFs) of S1A_1K and geophysical factors. Figure 6: The temporal evolution of corresponding principal components (PCs) for EOFs of S1A_1K. Figure 7: The temporal evolution of the corresponding PCs for the EOFs of normalized difference vegetation index (NDVI) and temperature. moisture, especially in the spatial dimension. Before this could be done, an assessment needed to be undertaken to determine which of the products could better represent the in situ measurements. As is shown in the results of Section 3.1.1, SMAP and SMOS are closer to the in situ soil moisture than ASCAT and AMSR2. ASCAT has a higher _ubRMSD_ in grassland than SMOS and SMAP [56; 57; 58], and more fluctuations of ASCAT soil moisture are observed in desert regions [57]. Similar fluctuations can also be seen in the temporal trend of Figure 3, especially in the dry season. AMSR2 has been reported [57] to show some overestimation in dry regions, and shows a poor performance in semiarid areas when compared with SMAP and SMOS. It also appears to largely disagree with SMAP in the soil moisture spatial patterns [59; 37; 57], which is a phenomenon that is also observed in both the dry and wet seasons in our study. SMAP and SMOS have been reported to be the most similar, exhibiting the lowest _ubRMSD_ values and the highest \(R\) values overall [57; 60]. In this paper, SMAP and SMOS obtain _ubRMSD_ values of 0.038 and 0.029 m\({}^{3}\)/m\({}^{3}\), respectively, and \(R\) values of 0.79 and 0.77, respectively, which are the lowest _ubRMSD_ values and the highest \(R\) values among all of the products. Combining the results of previous studies [56; 57; 58; 59; 60; 37], we can conclude that SMAP and SMOS are better indicators of in situ measurements for the further evaluation of S1A soil moisture in the spatial dimension. Although S1A_1K soil moisture is expected to be closer to in situ measurements because of the spatial scale, S1A_1K does not perform as well as SMAP and SMOS. As the dominant land-cover class at the Dahra site is grassland, the performance of S1A_1K could be mainly affected by the vegetation. Figure 8 shows the evolution of the backscattering coefficients and NDVI of the Dahra site, where the values in red circles correspond to severe overestimations of S1A_1K. While the NDVI is generally low in the dry seasons, the marked backscattering coefficients are even higher than the values in the wet seasons, which leads to the overestimations of S1A_1K.