## 3 Results
### Model Interpretability and Feature Importance  
To investigate the basis of our model's decisions, we adopted SHAP (Shapley Additive exPlanations) to conduct a feature importance analysis on the trained BO-Stacking-TabNet model. Unlike methods that rely on a model's internal structure, SHAP is founded on cooperative game theory, providing a fair, consistent, and model-agnostic quantification of the marginal contribution of each feature to the final prediction. This approach serves as a generally accepted standard for explaining complex models.  
The SHAP analysis result is shown as Figure 7, which revealed the key features that were most influential in classifying cultivated land quality. Among all input features, spectral data from August and October were identified as the most critical periods. Specifically, the B6 band from August (SR_08_B6), the B7 band from October (SR_10_B7), and the B1 band from April (SR_04_B1) exhibited the highest mean absolute SHAP values. This indicates they were the core drivers of the model's decisions. The high importance of bands from August, corresponding to the peak crop growing season, and October, the post-harvest period, aligns well with established phenological principles for assessing land quality, as these periods provide vital information about vegetation health and exposed soil conditions, respectively.  
Beyond this global contribution analysis, we further explored TabNet's native feature importance, which is derived from its internal attention masks, and the result is shown as Figure 8. The purpose of this second analysis is not to replace the SHAP results, but to gain a deeper insight into the internal working mechanism of our specific BO-Stacking-TabNet  
Figure 6: CLQ prediction error geographic analysis.  
model. This importance measure reflects the model's attention or how frequently it utilizes a feature across its sequential decision-making steps.  
A comparative analysis of the two methods provides a more profound understanding of the model's behavior. We observed a strong consensus in the key predictive periods,  
Figure 8: Feature importance ranking of Landsat 9 spectral bands during typical periods based on the BO-Stacking-TabNet model.  
Figure 7: Feature importance ranking of Landsat 9 spectral bands during typical periods based on the SHAP.  
with both SHAP and TabNet's internal mechanism highlighting the significance of features from October and August. For instance, the B5 band from October (SR_10_5) ranks highly in both analyses. This consistency across two distinct methodologies provides strong, cross-validated evidence for the critical role of these features.  
More interestingly, the comparison reveals the unique architectural advantages of TabNet. Some features with high contribution scores in the SHAP analysis, for instance, the B3 band from October (SR_10_B3), were assigned relatively lower weights by TabNet's attention mechanism. This is not a contradiction but rather a demonstration of TabNet's efficiency and sparse feature selection. Through its sequential, multi-step decision process, the model learns to make predictions using sparse combinations of features. Once the model has extracted sufficient information from certain features in early decision steps, it may no longer need to heavily attend to other highly correlated or information-redundant features in subsequent steps.