## 3 Results
### Spatial Prediction of STN Content  
The spatial distribution maps and corresponding descriptive statistics of STN content predicted by the RF, BRT, and SVM methods using Model E are shown in Figure 3 and Table 5, respectively. Figure 4 shows STN maps predicted using Model D constructed by using only remote sensing variables. The mean (\(\pm\)SD) values of the STN content predicted by the RF, BRT, and SVM methods were 0.81 (\(\pm\)0.23), 0.83 (\(\pm\)0.30), and 0.83 (\(\pm\)0.19) g kg\({}^{-1}\), respectively. The average and SD values of the STN content predicted by the three methods were lower than those of the observed STN content data. This result indicates that the predicted STN content is less variable than the measured STN. In addition, this result is consistent with the findings of [PERSON] et al. (2019) and [PERSON] et al. (2019). Since the performance of tree-based models (RF and BRT) was significantly better than SVM, we used RF and BRT methods to further explore the differences between STN content predicted by Model E and Model D. Figure 5a,b show the difference between STN content predicted using Model E and Model D (Model E-Model D). The mean (\(\pm\)SD) of these differences in STN content predicted by the RF and BRT methods was \(-\)0.03 (\(\pm\)0.07) and \(-\)0.01 (\(\pm\)0.13), respectively, indicating that there was only a small difference between the STN content predicted by Model E and Model D in most of the study areas. There was a relatively high difference between the STN content predicted by Model E and Model D in barren land and vegetation coverage areas (see Figure 5c).  
Figure 3: Distribution maps of STN content obtained using Model E with RF (**a**), BRT (**b**), and SVM (**c**) modeling techniques. Model E included all environmental variables for modeling (LULC, remote-sensing derived variables, and topographic and climate variables).  
Figure 2: The relative importance of each predictor variable calculated using Model E for the RF (**left**) and BRT (**right**) modeling techniques. TWI, topographic wetness index; BC_1, backscatter coefficient of Sentinel-1A image on 28 th February 2015; BC_2, backscatter coefficient of Sentinel-1A image on 21 st June 2015; BC_3, backscatter coefficient of Sentinel-1A image 25 th September 2015; BC_4, backscatter coefficient of Sentinel-1A image 26 th October 2015; Model E included all environmental variables for modeling (LULC, remote sensing-derived variables, and topographic and climate variables).  
Figure 4: Distribution maps of STN content obtained using Model D with RF (**a**), BRT (**b**), and SVM (**c**) modeling techniques. Model D included variables derived from Landsat-8 and Sentinel-1 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modeling Technique** & **Minimum** & **Maximum** & **Mean** & **Standard Deviation (SD)** \\ \hline RF & 0.27 & 1.30 & 0.81 & 0.23 \\ BRT & 0.10 & 1.61 & 0.83 & 0.30 \\ SVM & 0.21 & 1.36 & 0.83 & 0.19 \\ \hline \hline \end{tabular} Notes: STN, soil total nitrogen; Model E included all environmental variables for modeling (LULC, remote sensing-derived variables, and topographic and climate variables).  
\end{table}
Table 5: Descriptive statistics of the STN content predicted by the RF, BRT, and SVM modeling methods using Model E.