## 4 Discussion
However, based on our research and the comparison shown in Tables 4 and 5, it is evident that while models using vegetation indices as input perform well in cultivated land quality evaluation, models using spectral reflectance as input perform significantly better. Allmodels using spectral reflectance as input show noticeable improvements compared to those using vegetation indices, demonstrating the feasibility and effectiveness of using spectral reflectance for land quality evaluation at the field scale. Models based on spectral reflectance as input can bypass certain steps, such as the selection of characteristic bands or the calculation of vegetation indices, and directly evaluate cultivated land quality based on the spectral reflectance from remote sensing imagery. This greatly simplifies the process of remote sensing-based land quality evaluation. However, whether this direct spectral reflectance-based evaluation method is applicable to farm scales outside the northeastern black soil region remains to be further explored and studied. Our analysis of feature importance, presented in Figures 7 and 8, provides strong evidence for the model's robust learning mechanism. The findings revealed that spectral bands from August (peak growing season) and October (post-harvest bare soil) were most critical for the model's predictions. This quantitatively validates our data-driven selection of temporal windows and aligns with established agronomic principles, where crop physiological status and exposed soil conditions serve as powerful proxies for underlying land quality. The fact that the model independently identified these phenologically significant periods, without prior manual feature engineering, significantly strengthens the confidence in our proposed method. Furthermore, the comparison between the model-agnostic SHAP analysis and TabNet's internal attention mechanism highlighted the model's efficiency in utilizing a sparse feature set. This confirms that the BO-Stacking-TabNet architecture not only achieves high accuracy but also develops an interpretable and efficient decision-making process, which is a key advantage for practical applications. While this study successfully constructed a cropland quality evaluation model at Shuanghe Farm, we acknowledge its limitations, the most significant being the single-site nature of the research area. Due to the difficulty in obtaining validation data from other comparable regions, the model's generalization capability across broader geographical ranges has not been sufficiently verified. However, this limitation also charts a clear course for our future research directions. Our immediate plan is to apply the existing model directly to other farms in the Northeast Black Soil Belt to assess its zero-shot transfer capability and to conduct an in-depth analysis of the key factors causing performance degradation. To address the issue of model portability, we will further explore more advanced machine learning techniques. For instance, introducing Domain Adaptation methods, particularly through Adversarial Training strategies, and using a small amount of unlabeled or labeled data from a target region to fine-tune the model, could enable it to rapidly adapt to the data feature distributions of the new area. Furthermore, for regions with sparse data, Few-shot Learning technology also offers a highly promising solution for the rapid development of localized models. In the long term, this research lays the methodological foundation for establishing a long-term dynamic monitoring system for cropland quality. Our ultimate goal is to integrate multi-year remote sensing imagery and ground observation data to explore the model's temporal generalization capabilities, thereby making the critical leap from static evaluation to dynamic monitoring. Such a dynamic monitoring system will be able to provide more timely and robust decision-making support for the sustainable use and protection of black soil.