###### Abstract  
X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm\({}^{3}\)/cm\({}^{3}\) and 0.032 cm\({}^{3}\)/cm\({}^{3}\) at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation. K 202214404  
Synthetic aperture radar (SAR) has been used successfully in soil moisture estimation over agricultural regions. It can reveal the important feature of both bare and vegetated surface. Nowadays, many SARs operating at C, L and X-bands have been applied for agricultural monitoring. However, most studies of soil moisture retrieval at regional scale focus on the applications of C- and L-band SAR sensors [5; 6; 7]. So far, as an X-band SAR satellite, KOMPSAT-5 SAR images have been widely used in sea ice mapping [8; 9; 10], ship detection [11; 12; 13; 14], land cover classification [15], disaster situations such as earthquakes [16], flooding [17; 18; 19] and marine oil spills [20; 21; 22]. Few studies have used KOMPSAT-5 for soil moisture estimation and comparative studies using X-band data are still in great demand. For soil moisture retrieval over bare soil cover, particularly sparsely or lightly vegetated lands where X-band may present utility for mapping surface soil moisture, surface roughness is the main variable affecting soil moisture estimation accuracy using SAR observations. Many empirical and semi-empirical scattering models have been developed to reproduce radar backscatter coefficients based on soil moisture and surface roughness [23; 24; 25; 26; 27]. However, for dense vegetated regions, these models would only reproduce the soil component of the total scattering, while the vegetation canopy structure is typically also a key mechanism. Thus, many vegetation scattering methods have been proposed to obtain the total backscattering by combining scattering contributions of vegetation and underlying soil over densely vegetated cover [28; 29; 30; 31]. The water-cloud model (WCM) was developed for predicting vegetation backscattering and has been widely applied in soil moisture retrieval [31]. [PERSON] et al. [32] combined Oh model and WCM to obtain soil moisture using L-band SAR observations, which resulted in the RMSE < 2 dB and the correlation coefficient >0.6. [PERSON] et al. [33] investigated the feasibility of vegetation descriptors in WCM and yielded soil moisture retrievals with a correlation coefficient of 0.59 and an RMSE of 0.069 cm\({}^{3}\)/cm\({}^{3}\). [PERSON] et al. [34] used vegetation descriptors obtained from L-band radar data to parameterize WCM and monitor soil moisture.