While some have adopted machine learning techniques, the research into machine learning models remains insufficient, particularly in the area of remote sensing spectral data-based machine learning models, which lacks deeper exploration. Current research on cultivated land quality assessment driven by single-source remote sensing spectral data is still in the exploratory stage. Some studies have utilized two types of satellite remote sensing data for this purpose. For example, [PERSON] et al. [25] estimated the distribution of land quality using a machine learning model driven by crop growth time series spectral data, based on Landsat 8 and MODIS satellite remote sensing data. Some studies have used a single satellite remote sensing dataset to assess cultivated land quality. For example, [PERSON] et al. [26] established a link between the distribution of land quality and the net primary productivity (NPP) indicator derived from Landsat single-source remote sensing imagery. Similarly, [PERSON] et al. [27] used the MODIS-based Vegetation Photosynthesis Model (VPM) to calculate the gross primary productivity (GPP) and assess the productivity of cultivated land. However, these studies still require the spectral data extracted from remote sensing images to be further processed into derived remote sensing reflectance indicators, which demands considerable time and effort. Therefore, a research avenue worth exploring is to develop a cultivated land quality assessment model based directly on spectral reflectance data from single-source satellite remote sensing imagery, without the need to generate derived spectral indicators. However, these studies still require the spectral data extracted from remote sensing images to be further processed into derived remote sensing reflectance indicators, which demands considerable time and effort. In recent years, deep learning has achieved significant progress in precision agriculture and remote sensing interpretation. Various architectures have been widely applied to tasks such as land use classification, crop identification, and yield estimation. For instance, Convolutional Neural Networks (CNNs) like U-Net and DeepLabv3+ excel at processing spatial context from images, making them ideal for pixel-level semantic segmentation [28; 29]. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have proven advantageous for analyzing multi-temporal remote sensing data and capturing time-series features like crop phenology [30]. Furthermore, the Transformer architecture, especially the Vision Transformer (ViT), has emerged as a powerful new paradigm, showing great potential in remote sensing image analysis [31]. However, it is crucial to note that these models are predominantly designed for image data, excelling at extracting spatial patterns or temporal sequences. When the evaluation unit shifts from 'pixels' to the 'plot' or 'farm' scale, the input data is often aggregated into a tabular format (e.g., one row per plot containing mean reflectance values for multiple spectral bands). In such tabular data-driven scenarios, directly applying models designed for images may not be optimal, as the intra-plot spatial texture information has been averaged out. This motivates the exploration of deep learning models specifically designed for high-performance learning on tabular data. TabNet is one such model, architected with built-in feature selection and interpretability for tabular data [32]. It emulates decision tree-like behavior through a sequential attention mechanism, making it highly effective for tabular feature processing. Although TabNet and ensemble techniques like Stacking have been applied in some remote sensing tasks, such as crop mapping and soil property estimation [33; 34], their combined and optimized application within a framework for farm-scale cultivated land quality assessment--an ordinal classification problem using raw multi-temporal spectral reflectance directly--has not been systematically validated.