## 1 Introduction  
Food production and security have drawn the attention of scientific communities around the world. Crop growth simulation and yield monitoring are essential to inform and develop national food security policies and production management strategies [1; 2]. Climate change, environmental pollution, water scarcity, and change of cultivated land quality have serious effects on crop production [3]. Therefore, timely and accurate information on regional crop growth and yield is of great significance for food security and the sustainable development of agriculture. Traditional crop yield estimation mainly includes three methods, as follows: Statistical survey, agronomy prediction, and the agrometeorological forecast method. Due to their inherent limitations, it is difficult for these methods to achieve high precision estimations of regional crop yields [4]. The crop growth model is a process-oriented and machine-based dynamic model, which has been successfully applied to yield estimation at the field scale, but is not applicable for regional yield estimation. As a near real time technology, remote sensing can provide crop and soil parameters at a large scale and at lower costs than the survey method [5], though it is not as mechanistic as the crop model. Data assimilation is a method that optimally combines crop growth models with remote sensing observations, providing an effective way to improve crop yield estimation at regional scales and it is recognized as the most promising approach for crop growth monitoring and yield estimation [1; 6; 7]. Remotely sensed data derived from both active and passive sensors are utilized to observe the crop, providing precise and timely information on the phenological status and development of vegetation to monitor crop growth [8]. As an important state variable, soil moisture provides linkage to the soil, atmosphere, and plant, and plays a significant role in hydrological, ecological, and biological processes [9; 10]. With the development of remote sensing technologies, soil moisture monitoring at different spatial and temporal scales has become possible [11; 12]. Surface roughness and vegetation variables are the two main factors influencing soil moisture retrievals on the basis of SAR (synthetic aperture radar) observations [13]. To overcome the difficulty of obtaining the multiple scattering between soil and vegetation in vegetated areas, many widely used vegetation scattering models have been proposed. These models, which can retrieve soil moisture in vegetation covered areas, include the Tor Vergata model [14], the Michigan microwave canopy scattering (MIMICS) model [15], and the water cloud model [16]. The potential of the water cloud model for correcting the vegetation effect in various types of terrain has been previously demonstrated [17; 18; 19; 20; 21]. Previous studies have shown that the biophysical parameters needed for the water cloud model to represent the scattering properties of vegetation can be estimated from optical remote sensing data [22]. Additionally, the complementary information from SAR and the optical data provide the possibility to retrieve soil moisture with a high level of accuracy [23; 24]. Several studies have focused on combining optical and remotely sensed SAR data for soil moisture inversion. [PERSON] et al. combined C-band Radarsat-2 and Landsat-8 OLI (Operational Land Imager) data to estimate the soil moisture in prairie areas [10], while [PERSON] et al. developed a modified vegetation backscattering model to retrieve vegetation covered soil moisture based on RADARSAT-2, GF-1, and ground observations [13], and the results were promising. Thus, in this study, we jointly used Sentinel-1 C-band SAR data and Sentinel-2 MSI optical data to retrieve soil moisture by using the water cloud model. Remote sensing of soil moisture is potentially useful for sequential data assimilation, because it has an obvious influence on crop growth and especially on crop yields.