## 4 Discussion
Nevertheless, S1A_1K reaches an acceptable accuracy, with a _ubRMSD_ of 0.053 m\({}^{3}\)/m\({}^{3}\) (RMSD of 0.061 m\({}^{3}\)/m\({}^{3}\)), when compared with other studies of soil moisture retrieval by Sentinel-1 images, where the RMSD of the Sentinel-1 soil moisture has ranged from 0.05 to 0.15 m\({}^{3}\)/m\({}^{3}\) and the \(R\) value has ranged from 0.7 to 0.9. S1A_0.25 has a comparable high accuracy, although the deviations are largely eliminated by the averaging. Both S1A_1K and S1A_0.25 show good agreements of temporal trend with the precipitation and microwave products in 2016 when there are no in situ measurements. Meanwhile, S1A_0.25 shows a similar spatial distribution to SMAP and SMOS in Figure 4, which indicates that the S1A soil moisture can be considered as close enough to the real condition in the spatial dimension. The performances of the S1A soil moisture not only proves the effectiveness of the retrieval method used in this study, which does not need training or calibration, but it also shows the potential for retrieving soil moisture using time-series Sentinel-1 data. Figure 8: The backscattering coefficients of S1A and the NDVI at the Dahra study site. The red circles correspond to overestimations of S1A_1K. After the evaluations in both the temporal and spatial dimensions, the spatiotemporal analysis using the EOF method could be applied to S1A_1K. According to Figures 5a and 6, the primary pattern of soil moisture distribution indicates that the southwest part is drier than the others, especially in the wet seasons. This pattern has higher correlations with soil texture than the other factors and the wetter pixels tend to have a higher fraction of clay, as shown in Figures 2a and 5a. The fraction of clay and sand define the infiltration and water holding capacity of the soil, and a higher proportion of clay in the soil will slow down the process of infiltration and hold more water content [32], which might be the reason for the clayey pixels having higher soil moisture values when the precipitation increases in the wet seasons. Since gravity is a driving force of the vertical flows of water into deeper levels of the ground, the topographic complexity of the study area can be assumed to impact the soil moisture pattern: for example, wetter locations tend to occur in the valley bottoms [61]. In this study site, the elevation shows no significant correlation with the distribution pattern of soil moisture. The main reason for this may be that the study area has a relatively smooth topography, which is not enough to impact the local accumulation or evaporation pattern of soil moisture. The vegetation factor is represented by the NDVI derived from MODIS. For a vegetated region, the vegetation layer covering the ground will restrict the evaporation of soil moisture and intercept the input water by the leaf, but it can also cause soil moisture loss by evaporation [32]. Medium but negative correlation is observed between the primary EOF of soil moisture and the NDVI. The primary EOF of the NDVI is dominant in the dry seasons and most of the wet seasons, with the pattern that higher values correspond to lower soil moisture pixels in the southwest and lower values correspond to higher soil moisture pixels in the other areas. This phenomenon may indicate that the loss of soil moisture is the dominant process in the vegetation region of this study area, whether by the interception of rain or restraining of the water into the soil through stem flow. Since the impact of vegetation on soil moisture is observed in Figure 8, this conclusion needs further validations. As for the temperature, it is the key factor that influences the loss of water by evaporation from soil or vegetation. However, the EOFs of temperature appear to have no significant correlations with the distribution pattern of soil moisture. Although the explanation rate of the primary EOF of temperature is rather high, no large differences in PC coefficients among the EOFs are observed.