## 3 Results
### Model Performance  
We constructed five STN content models based on different combinations of environmental variables: Model A represents environmental variables without SAR data while Model E was a combination of all environmental variables; Models B and C represent optical and SAR remote sensing variables, respectively; and Model D included variables derived from Landsat-8 and Sentinel-1 images. Table 4 shows the model performance statistics for the BRT, RF, and SVM techniques using these STN models. The BRT model obtained the highest accuracy to predict STN, with the highest R\({}^{2}\) (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. The predictive performance of the BRT and RF models was significantly better than the SVM model. However, the prediction accuracy of the BRT model was closely followed by the RF model (R\({}^{2}\) = 0.55, RMSE = 0.24, and MAE = 0.19). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modeling Technique** & **Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline BRT & Model A & 0.19 & 0.25 & 0.52 \\  & Model B & 0.23 & 0.29 & 0.37 \\  & Model C & 0.23 & 0.28 & 0.40 \\  & Model D & 0.19 & 0.24 & 0.55 \\  & Model E & 0.18 & 0.24 & 0.57 \\ RF & Model A & 0.19 & 0.25 & 0.52 \\  & Model B & 0.22 & 0.29 & 0.37 \\  & Model C & 0.22 & 0.27 & 0.44 \\  & Model D & 0.20 & 0.25 & 0.50 \\  & Model E & 0.19 & 0.24 & 0.55 \\ SVM & Model A & 0.20 & 0.26 & 0.47 \\  & Model B & 0.22 & 0.28 & 0.36 \\  & Model C & 0.22 & 0.28 & 0.40 \\  & Model D & 0.21 & 0.27 & 0.44 \\  & Model E & 0.20 & 0.25 & 0.50 \\ \hline \hline \end{tabular} Notes: Model A was a combination of LULC, Landsat-8-derived variables, and topographic and climate variables; Models B and C included variables derived from Landsat-8 and Sentinel-1 images, respectively; Model D included variables derived from Landsat-8 and Sentinel-1 images; Model E included all environmental variables for modeling (LULC, remote sensing-derived variables, and topographic and climate variables).  
\end{table} 
Table 4: Comparison of predictive performance of combinations of different environmental variables based on boosted regression trees (BRT), random forest (RF), and support vector machine (SVM) modeling techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Minimum** & **Maximum** & **Mean** & **Median** &
\begin{tabular}{c} **Standard** \\ **Deviation (SD)** \\ \end{tabular} & **Skewness** \\ \hline STN (g kg\({}^{-1}\)) & 0.06 & 1.68 & 0.87 & 0.96 & 0.34 & \(-\)0.33 \\ Elevation (m) & 1347.59 & 1757.26 & 1509.59 & 1499.60 & 111.59 & 0.46 \\ Aspect (degree) & 0 & 355.42 & 145.67 & 122.98 & 104.50 & 0.32 \\ Slope (degree) & 0.25 & 10.64 & 3.62 & 3.08 & 2.25 & 0.85 \\ TWI & 5.42 & 15.90 & 8.89 & 8.19 & 2.60 & 0.79 \\ BC\_1 (dB) & \(-\)22.69 & \(-\)1.21 & \(-\)13.41 & \(-\)13.70 & 3.22 & 1.34 \\ BC\_2 (dB) & \(-\)17.26 & \(-\)3.67 & \(-\)9.59 & \(-\)9.48 & 2.47 & \(-\)0.61 \\ BC\_3 (dB) & \(-\)18.09 & \(-\)1.06 & \(-\)9.43 & \(-\)9.02 & 2.37 & \(-\)0.25 \\ BC\_4 (dB) & \(-\)23.40 & \(-\)7.10 & \(-\)16.36 & \(-\)16.49 & 3.16 & 0.30 \\ band\_4 (digital number) & 290.26 & 2921.85 & 1095.11 & 832.58 & 722.24 & 0.71 \\ band\_5 (digital number) & 2055.74 & 4960.55 & 3445.53 & 3586.26 & 672.64 & \(-\)0.19 \\ band\_6 (digital number) & 1324.40 & 3936.99 & 2217.60 & 2016.11 & 695.99 & 0.85 \\ NDVI & 0.03 & 0.88 & 0.53 & 0.60 & 0.28 & \(-\)0.37 \\ MAP (mm) & 90.04 & 150.13 & 112.58 & 110.25 & 18.32 & 0.51 \\ MAT (degrees Celsius) & 5.96 & 8.04 & 7.36 & 7.44 & 0.56 & \(-\)0.79 \\ \hline \hline \end{tabular} Notes: STN, soil total nitrogen; 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.  
\end{table} 
Table 3: Statistical results of STN content and environmental variables at sampling points.