## 3 Experimental Result
### Comparative Experiment
For the most divergent indicator, K, ReSE-AP Net improved upon PLSR and RF by approximately 18.63% and 41.79%, respectively. Compared with deep-learning baselines, ReSE-AP Net surpassed the Transformer model on half of the evaluated metrics (clay, silt, sand, OC, CaCO\({}_{3}\), and P) and matched it on the remaining metrics, underscoring the proposed network's superior capability for multi-element hyperspectral prediction. With respect to the RMSE metric, PLSR again delivered the lowest prediction errors among the traditional machine learning models, followed by SVR. Within the deep learning cohort, the Transformer architecture achieved the best RMSE on over half of the evaluated indicators, with VGG-11 ranking second. In contrast, ReSE-AP Net consistently surpassed all of the baseline models. For the most closely matched indicator, phosphorus (P), ReSE-AP Net reduced the RMSE by approximately 14.2% relative to the best machine learning performer (PLSR) and by about 25.2% relative to the poorest performer (RF). For calcium carbonate (CaCO\({}_{3}\))--the indicator exhibiting the largest discrepancy--ReSE-AP Net lowered the RMSE by roughly 41.9% compared with PLSR and by 69.2% compared with RF. Against the deep learning baselines, ReSE-AP Net outperformed all counterparts on every indicator except for the pH in H\({}_{2}\)O, where it tied with TCN. For silt--the closest indicator among the deep models--ReSE-AP Net achieved a further 1.6% reduction in RMSE over the best deep model (VGG-11) and a 24.4% reduction relative to the worst (ResNet-152). For the pH in CaCl\({}_{2}\), which showed the greatest gap, ReSE-AP Net lowered the RMSE by about 1% compared with TCN and by approximately 39.0% compared with ResNet-152. It is worth noting that on the RMSE indicator, the means of other models, except for ReSE-AP Net, on the 11 elements were respectively: 6.526% (clay), 11.838% (silt), 15.494% (sand), 0.584 (PH in CaCl\({}_{2}\)), 0.555 (PH in H\({}_{2}\)O), 24.611 g/kg (OC), 41.107 g/kg (CaCO\({}_{3}\)) 1.293 g/kg (N), 28.261 mg/kg (P), 190.939 mg/kg (K), and 7.876 cmol(+)/kg (CEC). ReSE-AP Net increased the mean value of each element by approximately 26.9% (clay), 22.2% (silt), 25.7% (sand), 39.5% (PH in CaCl\({}_{2}\)), 34.6% (PH in H\({}_{2}\)O), 31.4% (OC), 44.8% (CaCO\({}_{3}\)), 28.7% (N), 17.9% (P) 21.8% (K), and 21.5% (CEC). As illustrated in Figures 5 and 6, Figure 5 compares the coefficient of determination (R\({}^{2}\)) obtained by the competing models for each soil attribute, whereas Figure 6 presents the corresponding root mean square error (RMSE). In Figure 5, the closer a model's curve was to the upper boundary, the better its predictive performance; conversely, in Figure 6, curves located nearer the lower boundary indicated smaller errors and hence superior accuracy. Within the same plot, greater separations between the two curves signified more pronounced performance disparities. The results revealed that with the exception of the P, K, and CEC indicators, ReSE-AP Net occupied the uppermost--or nearly uppermost--position across all remaining attributes in Figure 5. Likewise, in Figure 6, the ReSE-AP Net curve lay at the very bottom for almost every element, underscoring its outstanding overall predictive accuracy.