###### Abstract
The results showed that the overall accuracy of retrieved soil moisture was promising with unbiased RMSE of 0.056, 0.053, 0.098 and 0.079 cm\({}^{3}\)/cm\({}^{3}\) for canola, corn, bean and wheat, respectively. In order to optimize parameters in WCM and improve the accuracy of soil moisture estimation, many modified and calibrated models based on WCM have been developed by introducing various vegetation indices to represent the effect of vegetation on total scattering more precisely. [PERSON] et al. [35] applied a modified WCM with the least squares method to construct a newly developed model based on study of the effect of vegetation backscattering under different vegetation indices and soil moisture values. The most significant relationship was found for data with the lowest incidence angle, acquired from the ascending tracks of Sentinel-1 satellite in the Biebrza Wetlands, situated in north-eastern Poland. The soil moisture estimation for wetlands showed a reasonable accuracy (10 vol.%), which is several times higher than in agricultural areas. [PERSON] et al. [36] investigated a modified WCM using crop coefficients by minimizing the RMSE between observed and simulated backscatter coefficients in VV polarization. EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were treated as vegetation descriptors and used for the parametrization of vegetation and soil coefficients of modified WCM. Comparison between observed and simulated backscatter coefficients in VV polarization showed strong R\({}^{2}\) and low RMSE with the combined use of NDVI and EVI. [PERSON] et al. [37] proposed a modified vegetation backscattering model by considering scattering influences of the vegetation canopy and underlying bare soil using RADARSAT-2 SAR data and field measurements. A vegetation parameter named vegetation coverage, which can be calculated from NDVI, was introduced into the modified backscattering model to effectively estimate the scattering mechanisms of vegetation. The modified model performed well with R\({}^{2}\) of 0.806 and RMSE of 0.043 cm\({}^{3}\)/cm\({}^{3}\). However, in WCM or other modified models, the exact expression of soil moisture retrieval is difficult to solve mathematically due to the complex relationship between vegetation parameters, soil moisture and total backscatter coefficient. Thus, nowadays,statistical methods such as change detection and machine learning approaches are widely used to implement the retrieval processes [38; 39; 40; 41]. The change detection algorithm was first proposed by [PERSON] et al. to estimate temporal soil moisture over vegetated regions [38]. NDVI has been used to represent the scattering influences of vegetation on the SAR signal. Although this simple method can reduce the impact of input parameter uncertainty effectively, surface roughness still strongly affects the minimum backscatter coefficient. The method is more suitable for grassland cover with constant roughness. The machine learning approaches such as artificial neural networks (ANNs) and ensemble learning have become a popular tool for implementing flexible inversion algorithms and have been widely used to address many retrieval problems in the soil moisture retrieval from SAR observations [39; 42; 43]. However, a large number of simulated datasets for training the ANNs are needed to obtain the best retrieval accuracy. In this work, we propose a new vegetation scattering model and make it more accurate to retrieve soil moisture using the SAR and optical data under wheat-covered conditions in the study area. This model is derived from a modified backscattering model proposed by [PERSON] et al. [37] and a numerical relationship between soil moisture and radar signal is obtained. A new vegetation index is proposed to obtain the scattering contributions of the wheat canopy in the total backscattering. Soil moisture contents estimated from KOMPSAT-5 and Sentinel-1a/b are evaluated against ground-based soil moisture measurements over agricultural fields.