## 4 Discussion  
### Model Performance  
Our findings showed that tree-based models (RF and BRT) can achieve better STN prediction accuracy than SVM. This was consistent with the results of [PERSON] et al. 's [5] soil property prediction study in Australia, which reported that the prediction accuracy of RF and BRT models was higher than that of SVM. [PERSON] et al. [51] compared the accuracy of BRT, SVM, and ANN models to predict soil properties in Belgium, and found that the tree-based model (BRT) performed best. [PERSON] et al. [52] used different models to map soil properties in the Kastoria area and found that the RF model improved the prediction accuracy compared to ordinary kriging (OK). However, the results of [PERSON] et al. [53] showed that the SVM model was superior to RF in SOC prediction. [PERSON] et al. [41] found that the RF model was superior to Cubist in SOC mapping in Brazil while [PERSON] et al. [54] reported that Cubist performed better than RF in SOC prediction. Although these studies have different comparison results, other digital soil mapping studies in the HRB have also found that the prediction performance of RF models was better than SVM [55,56]. Based on these results, no single machine learning technique is most suitable for all landscapes. Therefore, it is necessary to evaluate and compare the prediction capabilities of different models under different landscape and environmental input variables. The prediction accuracy (Table 4) of Model C constructed by multi-temporal Sentinel-1A data indicates that the model could explain 44% of the STN variation. Compared with a model constructed  
Figure 5: Differences in STN content predicted by Model E and Model D (Model E-Model D) using RF (**a**) and BRT (**b**) modeling techniques; (**c**) The Landsat-8 OLI composite image (RGB = 432). 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). by RapidEye (optical imaging) to perform an STN prediction study in India (R\({}^{2}\) = 0.41) [57], the prediction performance of the model generated by multi-temporal SAR data in this study was not inferior. [PERSON] et al. [58] reported a study in Tibet that found that NDVI explained 6% to 29% of the soil nutrient stoichiometry. In our study, the inclusion of multi-temporal Sentinel-1A data improved the prediction accuracy of the STN. This is expected because the addition of more useful information improves the prediction accuracy of the model. Therefore, our results demonstrate the effectiveness of multi-temporal SAR data on STN mapping in an agro-ecosystem and has the potential to improve prediction accuracy. Some previous studies have also demonstrated the effectiveness of adding other useful information to digital soil mapping. For example, [PERSON] et al. [5] evaluated the effect of the addition of seasonal fractional cover data on SOC mapping in eastern Australia, achieving an improvement in the RMSE of 2.8% to 5.9%. The studies carried out by [PERSON] et al. [49] and [PERSON] et al. [59] respectively evaluated the effects of cultivation history and crop rotation information on predicting soil properties and demonstrated the effectiveness of such information on soil mapping. The most accurate model (Model E) of this study used RF and BRT modeling techniques to explain 55% and 57% of the STN variation, respectively (Table 4). Although the environmental conditions, sampling strategies, and verification methods of this study were different from those of the previous STN prediction studies, the prediction performance of the RF and BRT methods in this study was not inferior compared to these studies. For example, [PERSON] et al. [60] also developed an RF model for soil property prediction in Burkina Faso, but it only explained less than 40% of the STN variation. [PERSON] et al. [61] used the RF model for STN mapping in southern India to explain less than 50% of STN variation.