## 6 Conclusions  
In this paper, KOMPSAT-5 and Sentinel-1 radar data were compared to explore the effectiveness and potential in soil moisture estimation over vegetated areas. A new semi-empirical scattering model was used based on a vegetation scattering model and a vegetation water content mode. CVI is introduced to parameterize the vegetation water content and remove the effects of vegetation on radar signals in the proposed model. Results showed that this study effectively revealed the mechanism of radar signal and vegetation on soil moisture retrieval and established the exact nonlinear relationship between backscatter coefficient and soil moisture. The semi-empirical model presented the expression of soil moisture retrieval and solved it mathematically to obtain continuous spatial distribution of soil moisture. Meanwhile, compared with change detection and machine learning approaches, a small number of simulated datasets was enough to realize the inversion process of soil moisture and improve the inversion accuracy in this study. As illustrated above, soil moisture was obtained with R of 0.862 and 0.616 using KOMPSAT-5 and Sentinel-1 radar data. The RMSEs were 0.020 cm\({}^{3}\)/cm\({}^{3}\) and 0.032 cm\({}^{3}\)/cm\({}^{3}\) at X- and C-bands. The CVI and VV polarization combination method has a better soil moisture retrieval accuracy than NDVI and NDWI. Soil moisture mapping was generated based on SAR images and the results demonstrated that similar results were found between the estimated soil moisture using X-band SAR data and the in situ measurements. X-band SAR data are suitable for the soil moisture retrieval in agricultural areas. Meanwhile, considering the simplicity and practicality of the proposed model, it can be used to operationalize soil moisture retrieval and produce real-time soil moisture products.  
However, temporal gaps between the acquisition times of SAR and optical data may have introduced some errors in soil moisture retrieval. Errors from data preprocessingand field measurements can also seriously affect the accuracy of soil moisture retrieval. In addition, the semi-empirical model needs to be calibrated according to the soil type and vegetation growth status. Thus, in future work, more observations and ground measurements will be acquired to provide higher temporal resolution monitoring and validate the potential of X-band SAR data in soil moisture estimation. Meanwhile, soil type and vegetation growth status will be considered in field experiment to make the model more robust and applicable in other agricultural areas.  
Conceptualization, L.T. and D.R.; methodology, L.T.; software, L.T.; validation, L.T. and S.-G.L.; data curation, S.-G.L.; writing--original draft preparation, L.T.; writing--review and editing, D.R. and A.W.; funding acquisition, L.T. and D.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (41901278), by Science and Technology Project of Jiangsu Province (BK20180798), and by the University of Melbourne Research Contract #300405 sponsored by the Korea Aerospace Research Institute.  
Not applicable.