## 3 Results
### Model Performance Evaluation Based on Spectral Reflectance  
We used the spectral reflectance data from seven bands of Landsat 9 remote sensing images collected during four typical periods in 2022, namely April, May, August, and October. Without applying any band filtering, a total of 28 indicators were directly used as model inputs, with the spectral reflectance information for these seven bands presented in Table 2. The BO-Stacking-TabNet model, which was proposed in this study, was compared with other commonly used machine learning models in the field of remote sensing-based cultivated land quality assessment. All models were optimized using Bayesian optimization to achieve their optimal hyperparameters. The performance results of these models are displayed in Table 5. As shown in Table 5, the BO-Stacking-TabNet model achieved the best performance across all four metrics: Accuracy, Precision, Recall, and F1 score. The original, unmodified TabNet model also performed exceptionally well, ranking second in Accuracy, Recall, and F1 score. Outside of the TabNet and BO-Stacking-TabNet models, the Random Forest model showed strong performance in Precision, ranking second in this category. However, its performance in Accuracy, Recall, and F1 was relatively mediocre. The XGBoost model demonstrated stable performance across all four metrics (Accuracy, Precision, Recall, and F1 score). CatBoost performed similarly to XGBoost in terms of Accuracy and Precision but performed better than XGBoost in Recall and F1, showcasing good overall performance. In contrast, LightGBM had the weakest performance across all four metrics. Gradient Boosting Trees (GBT) performed well overall, showing better results than CatBoost in terms of Accuracy, Precision, Recall, and F1 score, albeit by a small margin. Overall, the BO-Stacking-TabNet model exhibited superior, comprehensive performance, while the original TabNet model also stood out, further validating the effectiveness of the improvements made in the BO-Stacking-TabNet model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Random Forest & 0.5849 & 0.6378 & 0.4326 & 0.4416 \\ Xgboost & 0.5590 & 0.4667 & 0.4695 & 0.4669 \\ Catboost & 0.5920 & 0.4625 & 0.4490 & 0.4524 \\ LightGBM & 0.5590 & 0.4300 & 0.4171 & 0.4210 \\ Gradient Boosting Tree & 0.5873 & 0.4510 & 0.4452 & 0.4453 \\ TabNet & 0.6109 & 0.4854 & 0.4804 & 0.4801 \\ BO-Stacking-TabNet & 0.6179 & 0.6814 & 0.4807 & 0.4917 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance evaluation based on typical vegetation indices as input variables. Therefore, from the above analysis, it can be concluded that only the TabNet model and BO-Stacking-TabNet model demonstrated consistently outstanding performance across all metrics. This confirms that the TabNet model also performs excellently in evaluating cultivated land quality based on spectral reflectance. Moreover, the BO-Stacking-TabNet model improved upon the TabNet model, with increases of 2.13% in Accuracy, 12.59% in Precision, 1.83% in Recall, and 2.19% in F1 score. These improvements clearly demonstrate the effectiveness of the enhancements made to the TabNet model in the BO-Stacking-TabNet. Additionally, as seen in Table 5, models using spectral reflectance as input variables showed significant improvements in Accuracy, Precision, Recall, and F1 score compared to models using vegetation indices as input. Specifically, the BO-Stacking-TabNet model using spectral reflectance achieved an increase of 10.62% in Accuracy, 1.55% in Precision, 11.05% in Recall, and 10.18% in F1 score compared to the BO-Stacking-TabNet model using vegetation indices as input. This significantly demonstrates the reliability of using Landsat 9 spectral reflectance for farmland-scale cultivated land quality assessment.