###### Abstract  
Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu'an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical-horizontal (VH) polarization, vertical-vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R\({}^{2}\)) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm\({}^{3}\)/cm\({}^{3}\), and the MAE was 0.023 cm\({}^{3}\)/cm\({}^{3}\). Thus, this methodology has the potential for SSM estimation in vegetated areas. Sentinel-1; Sentinel-2; surface soil moisture; water cloud model; red-edge band; vegetation water content 20221393  
is constantly being improved. It has become one of the indispensable means of regional soil moisture monitoring [13; 14; 15; 16; 17]. Remote sensing methods include two categories, namely optical and microwave remote sensing. Optical remote sensing is susceptible to cloud, fog, rain, and ground vegetation, which often leads to poor data quality and low availability, especially in southern China. Thus, optical remote sensing methods are generally applicable only for SSM estimation in bare or free vegetation-covered areas and under good weather conditions. In contrast, microwave remote sensing at longer wavelengths has a stronger penetrating ability that is not affected by meteorological conditions. It can be used to monitor various surface parameters, including SSM, over vegetated areas [18] and has certain advantages in SSM estimation [19]. Sentinel-1 (S1), which can provide near-real-time synthetic aperture radar (SAR) images, is widely used in agricultural monitoring. Based on S1 SAR images, [PERSON] et al. [20] developed an SSM mapping algorithm based on the artificial neural network (ANN) method. The results showed that the validation results were very much in line with the Global Monitoring for Environment and Security (GMES) requirements, which indicated a good correlation between estimated and in situ measured SSM values. [PERSON] et al. [21] presented an improved convolutional neural network (ICNN) method through combined multipolarized SAR and original multispectral images, which achieved regional SSM information with an average root mean square error (RMSE) of 0.0145 cm\({}^{3}\)/cm\({}^{3}\). Most of the developed methods can achieve desirable results in bare or sparse vegetation-covered areas, but the precision is not ideal for SSM estimation in vegetated areas. The water cloud model (WCM) is a commonly used semi-empirical model for SSM retrieval in vegetated areas [22]. Based on the WCM, previous studies [23; 24] estimated SSM from C- and X-band SAR with an accuracy better than 8 vol%. [PERSON] et al. [25] used S1 and Landsat-8 images to retrieve SSM over vegetated areas based on the modified water cloud model (MWCM). The results showed a good correlation between estimated and in situ measured SSM. In the WCM, the backscattering coefficient includes soil contribution and direct vegetation contribution. The vegetation backscattering coefficient is calculated by one or more vegetation descriptors.