As a typical active microwave remote sensor, synthetic aperture radar (SAR) has a finer spatial resolution than the passive microwave remote sensors [12; 13]. SAR data have also been widely used in soil moisture retrieval, especially for small-scale studies. Soil moisture retrieval using SAR data is confronted with challenges. Apart from the soil dielectric properties, the backscattering signal of SAR is also impacted by the soil roughness and the parameters of the vegetation layer. The complex and non-linear relationship between the signal and the surface properties makes soil moisture retrieval an ill-posed problem [14; 15; 16]. Previous studies have found that using multi-frequency, multi-polarization, and multi-temporal observations can improve the retrieval results [17; 18; 19; 20]. Since 2014, the two-satellite constellation of the Sentinel-1 mission, as designed by the European Space Agency (ESA) has provided a six-day repeat cycle, together with a high spatial resolution, large swath width, long-term continuity, and rapid data dissemination [21]. With shorter revisit time, the Sentinel-1 mission is expected to reduce the ill-posed retrieving using the time-series data. Methods such as change detection [22], Bayesian approach [23], and Artificial Neural Nets (ANN) [24] have been developed for the Sentinel-1--before launching. These researches conceived to process systematically the Sentinel-1 data and produce the soil moisture products at 1 km resolution or less in near real time (NRT). After the official release of the Sentinel-1 data, [PERSON] et al. [4] assessed the Sentinel-1A soil moisture over Tibetan Plateau using the look-up table (LUT) of water cloud model (WCM) and advanced integrated equation model (AIEM), which reached the root-mean-square deviation varying from 0.05 to 0.15 m\({}^{3}\)/m\({}^{3}\) in different stations; the authors also investigated the effective roughness parameters and uncertainties in the LAI on soil moisture retrieval. [PERSON] et al. [25] tested soil moisture retrieving by ANN with multi-temporal Sentinel-1 and Landsat 8 images, yielding R\({}^{2}\) values between 0.7 and 0.9, and the estimated soil moisture were used for the hydrologic simulation of a flow event. Up to now, Sentinel-1 has shown its potential on soil moisture retrieval, while the retrieving performances still need more evaluations. As the data continue to accumulate, studies of soil moisture retrieval using Sentinel-1 data will increase and attract more and more attentions. The Sentinel-1 mission also provides us with an opportunity to investigate the spatiotemporal patterns of soil moisture. Methods such as geostatistical analysis, wavelet analysis, temporal stability analysis, and empirical orthogonal functions (EOF) analysis have been used to characterize and analyze the spatial and temporal variability of soil moisture [26]. Among these different methods, the EOF method can detect the spatiotemporal patterns of large multidimensional datasets by reducing the dimensionality of the data sets while retaining most of the information. As a result, the EOF method has been widely used in meteorology, geology, and hydrology [27]. However, previous studies of the spatiotemporal patterns of soil moisture have generally focused more on ground-based measurements rather than remote sensing datasets, as in situ point observation is considered as the most straightforward and accurate approach [28; 29]. The remote sensing datasets used in soil moisture spatiotemporal studies are mostly from research-oriented field campaigns. [PERSON] and [PERSON] [30] used the EOF method to analyze the physical controls on near surface soil moisture variability at the field scale during the Soil Moisture Experiments in 2002 (SMEX02) campaign, and found that 81% of the total variability could be explained by the four EOFs that are related to soil texture, vegetation, rainfall, and topography.