## 4 Discussion
### Importance of Predictor Variables  
Our results not only indicate the importance of remote sensing data to predict STN but also emphasize the need to incorporate SAR images. Radar can provide spectral information beyond vegetation cover and soil surface [12], and backscatter signals from SAR images are used to retrieve target properties, such as forest above-ground biomass, soil texture, soil moisture, and salinity. Many studies have shown that information from SAR data, such as the backscatter coefficient, can detect vegetation [64] and soil moisture [65]. There is a significant positive correlation between STN and vegetation in the topsoil [66], and the backscattering coefficient is an important indicator representing vegetation density and biomass. [PERSON] et al. [23] reported that Sentinel-1 images successfully predicted soil properties because of their ability to capture characteristics of short-term vegetation changes. [PERSON] et al. [22] found that soil properties were related to vegetation canopy detected by remote sensing images because the soil was strongly affected by the vegetation. Although Sentinel-1 images have rarely been used as predictor variables for mapping STN, previous studies using these images to monitor vegetation demonstrated the ability of SAR data to capture vegetation information, which can be further applied to map STN because of the relationships in the soil-vegetation system. For example, [PERSON] et al. [67] found that the combination of Sentinel-1 and Sentinel-2 obtained the best above-ground biomass monitoring results while [PERSON] et al. [68] used Sentinel-1 to obtain satisfactory soil moisture inversion results on the Loess Plateau of China. A variety of optical satellite images have been applied to soil mapping, such as Landsat, Sentinel-2, and Pleiades-1A. The optical remote sensing variables commonly used for mapping STN are spectral reflectance and derived vegetation indices [69]. Spectral reflectance, which is closely related to vegetation density and biomass, and LULC are important environmental variables that affect STNcontent. Soil and vegetation have interactive effects, such as spatial and temporal changes in soil nutrients due to the accumulation and decomposition of vegetation biomass. [PERSON] et al. (2016) found that spectral reflectance is one of the main factors affecting STN variation. [PERSON] et al. (2017) reported that optical remote sensing-derived variables were the most important environmental variables affecting STN variation compared to topographic and climate variables. However, this finding differed from our results, mainly due to the addition of multi-temporal Sentinel-1A images to map STN in this study. LULC patterns are one of the most direct and important factors affecting soil nutrient changes. Many studies have shown that different LULC patterns have a greater impact on soil nutrient content ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019). Consistent with the findings of [PERSON] et al. (2017), it also reported that the LULC data obtained the highest relative importance. In addition, the results of [PERSON] et al. (2017) and [PERSON] et al. (2017) showed that soil nutrient content in the HRB has strong spatial variation in different LULC types. As one of the five soil formation factors, terrain indirectly affects the soil by causing redistribution of matter and energy. Therefore, topographic variables are closely related to the spatial variation of soil properties and are often used as a key predictor for digital soil mapping ([PERSON] et al., 2018; [PERSON] et al., 2019). Among all topographic variables, elevation achieved the highest relative importance. Elevation can affect the microclimate at local scales and indirectly affect microbial activity, affecting the decomposition and transformation of STN ([PERSON] et al., 2019).