## 4 Discussion
This means that there is no dominant pattern of temperature in this study area, which may result in the reduced contribution to soil moisture patterns. Previous studies have generally considered that soil moisture spatial variability is mainly affected by meteorological forcings, such as precipitation and temperature at the scale from 50 to 400 km [62; 63; 64], which is contradictory to the result in our study. As reported by [PERSON] et al. and [PERSON] et al. [27; 65], the effect of meteorological forcings on regional soil moisture spatial variability might become more important in regions with larger climate gradients or more homogeneous soil textures. Therefore, further studies are still necessary for the understanding of the geophysical controls on soil moisture patterns. Although the ongoing microwave products were used for evaluation, we still cannot provide a comprehensive evaluation of S1A soil moisture (especially at 1-km resolution) due to the difference in the spatial resolution. The robustness of the retrieval algorithm that was used in this study can be further investigated using soil moisture from a land surface model or a well-designed ground sensor network as the reference. As long-term ground observations of soil moisture are increasing in number [66; 67], the optimization of the sensor network starts to attract more attention. The ground measurements can be expected to represent the variability of the soil moisture in the study site [68]. Since the spatial and temporal soil moisture variability is affected by the heterogeneity of soil, vegetation, and topography [26; 69], the sensor network should also have the ability of capturing the distribution of these factors. In our study area, the locations of additional sensors should be selected according to the distribution of soil moisture, soil texture, vegetation, and land covers, because the terrain is relatively flat and both the elevation and temperature have little impact on the patterns of soil moisture. Apart from different types of soil texture and land covers, the locations of the sensors could be selected with different soil moisture and NDVI levels, based on the primary spatial patterns of S1A_1K and NDVI, shown in Figure 5a,e. [PERSON] et al. [70] suggested that if the measurementsof soil moisture at the field were repeatedly observed, certain locations could be identified as being temporally stable. Thus, a sensor network with a temporal persistence of more than one year might provide more reliable records when considering the seasonal changes of some meteorological and geophysical factors (e.g., precipitation and vegetation, as shown in Figures 3 and 8). To identify the representative locations for the validation of remote sensing soil moisture, temporal stability analysis (TSA) has been widely used in previous research [71; 72; 73]. The basic principle of TSA is that a location that demonstrates the ability to capture the mean of the field with a small bias (low mean relative difference: MRD) and low variability (i.e., low standard deviation of the relative difference: SDRD) would be a representative station [74]. Since the EOFs and the corresponding PCs of S1A_1K represent different spatial patterns and their variability in the observation period, the EOF analysis might also be able to characterize the temporal stability of soil moisture. Therefore, further evaluation of Sentinel-1 soil moisture based on a well-designed sensor network will be investigated in our future work.