## 4 Discussion  
### SSM Estimation in Vegetated Areas  
Many RS-based models are available for SSM retrieval [43; 44; 45; 46; 47; 48]. However, these models have limited potential to estimate SSM directly over vegetation-covered areas due to the distributed scattering of vegetation. Microwave RS can be widely used in SSM estimation  
Figure 8: SSM mapping on two different dates: 28 April 2019 (a), and 29 April 2021 (b), in Gu’an, Hebei, China.  
Figure 7: SSM mapping on two different dates: 28 April 2019 (a), and 29 April 2021 (b), in Daxing, Beijing, China.  
because it is not affected by weather or light and has strong penetration, which can improve reliability and effectiveness. However, in vegetation-covered areas, the attenuation and scattering of microwave radiation signals from soil by the vegetation layer and radiation from the vegetation itself will influence the accuracy of SSM estimation. In the research of SSM retrieval, removing the effect of the vegetation layer has always been the focus, and some progress has been made.  
In this paper, the WCM was used to estimate SSM in vegetation-covered areas, and satisfactory results were obtained, with an R\({}^{2}\) of 0.867, MAE of 0.023 cm\({}^{3}\)/cm\({}^{3}\), and RMSE of 0.028 cm\({}^{3}\)/cm\({}^{3}\). The results were close to those obtained in other studies. [PERSON] et al. [40] used an improved water cloud model to estimate SSM in partial vegetation-covered areas in UK and Spain and found that the modeled value had a high consistency with the in situ measured value (R\({}^{2}\) = 0.829); this value was slightly lower than the validation obtained in the present study (R\({}^{2}\) = 0.867). This difference is mainly due to differences in the used optical RS data and the number of verification points. [PERSON] et al. [14] used the WCM to estimate SSM in the Kairouan plain, and the validation results were relatively poor (R\({}^{2}\) = 0.4, RMSE = 0.064 cm\({}^{3}\)/cm\({}^{3}\)). However, it has been well applied in irrigation mapping. [PERSON] et al. [49] obtained regional SSM with a spatial resolution of 100 m in Urgell (Catalunya, Spain) using S1 SAR and S2 optical data based on change detection and found that the modeled value had a high consistency with the in situ measured value (RMSE = 0.087 cm\({}^{3}\)/cm\({}^{3}\)); this value was slightly higher than the validation obtained in the present study. This difference is mainly due to differences in the spatial resolution and methods used. [PERSON] et al. [50] developed the evaluation of an optical-passive microwave method for SSM estimation at the regional scale using RS and reanalysis data. The results contribute to the use of single linear and semi-empirical regressions to calculate SSM at the regional scale based on SMOS L-band bipolarized brightness temperature. [PERSON] et al. [51] developed an SSM retrieval algorithm from multisource fusion data through the sowing, jointing, heading, and flowering stages of maize. The results showed that the improved Dubois (IDubois) model was better than the Dubois model, based on the RMSE (RMSE\({}_{\text{IDubois}}\) = 0.0371 cm\({}^{3}\)/cm\({}^{3}\), RMSE\({}_{\text{Dubois}}\) = 0.0654 cm\({}^{3}\)/cm\({}^{3}\)); these values were slightly higher than the validation obtained in the present study (RMSE = 0.028 cm\({}^{3}\)/cm\({}^{3}\)). This difference may be caused by the different models used.  
Based on the above studies, it can be found that the improved WCM based on the vegetation red-edge band proposed in this research has improved accuracy with a high spatial resolution (20 m \(\times\) 20 m), which has a wide application prospect.