## 4 Discussion  
A scientific remote sensing-based method for evaluating cultivated land quality offers an efficient and quick way to understand the current status of land quality distribution, saving both labor and time. Remote sensing data has the advantages of wide coverage, easy accessibility, and rich information. Therefore, research on remote sensing-based evaluation of cultivated land quality has significant practical value. However, there is currently a lack of studies focusing on land quality evaluation at the farm scale, with most research in this area concentrating on larger scales. Our study, conducted in the black soil region of Shuanghe Farm in northeastern China, confirms the feasibility of applying remote sensing-based land quality evaluation at the farm scale. The regional validation of this study shows that the BO-Stacking-TabNet model, using Landsat 9 spectral reflectance from typical periods, produces classification results that are highly consistent with the spatial distribution and grade statistics of Shuanghe Farm's cultivated land quality dataset. This indicates that the method for evaluating cultivated land quality based on Landsat 9 spectral reflectance is both reasonable and feasible. Selecting the optimal remote sensing imagery period is a critical prerequisite for accurately evaluating cultivated land quality. The national standard 'Quality Grades of Cultivated Land' (GB/T 33469-2016) primarily bases its evaluation on intrinsic soil properties (e.g., physical, chemical, and nutrient characteristics) and topographical features. Consequently, imagery from bare-soil periods, when soil is maximally exposed, is traditionally considered ideal. However, rather than relying solely on this empirical assumption, this study implemented a systematic, data-driven experiment to quantitatively assess the performance of imagery from different key phenological periods. Our objective was to identify the most informative periods and understand the underlying reasons for their effectiveness. We selected imagery from April/May (post-thaw and pre-sowing), August (peak growing season), and October (post-harvest). The strong performance of models using imagery from April, May, and October (as shown in Table 5) aligns with the traditional approach. During these bare-soil or semi-bare-soil periods, spectral information from the Landsat 9 sensor can directly capture variations in soil organic matter, moisture, and texture, providing a direct assessment of the soil background. A particularly noteworthy finding is the high classification accuracy achieved using imagery from August, the period of maximum vegetation cover. While vegetation can obscure the direct soil signal, it also functions as a highly effective bio-indicator of underlying soil quality. The health, density, and vigor of the crop are integrated expressions of the soil's ability to provide water and nutrients. Our model successfully learned the complex relationship between the crop's spectral characteristics during its peak growth and the intrinsic quality grade of the soil it grows in. This demonstrates that the crop's phenotype serves as a robust proxy for the comprehensive land quality defined by the standard. This finding suggests that while bare-soil imagery reflects the static potential of the land, peak-season imagery captures its realized productive performance, offering a complementary and equally valuable perspective for evaluation. Therefore, our results provide a validated and more flexible set of temporal windows for remote sensing-based cultivated land quality assessment in the black soil region of Northeast China. Meanwhile, we acknowledge that our approach prioritizes classification accuracy over temporal representativeness, which may introduce temporal bias. Traditionally, many studies have used vegetation index indicators derived from the spectral reflectance of remote sensing imagery as input for model evaluations.