[6] used multi-temporal Sentinel-1 (S1) and Sentinel-2 imagery for crop mapping, showing that images from different growth stages provide a more comprehensive representation of crop phenology than single-date data, improving classification accuracy. However, multi-temporal data still have drawbacks. [PERSON] et al. [8] created a 10 m resolution crop map of the Shiyang River Basin using multi-temporal S2 images from the 2019 growing season. They noted that manually selected phenological stages introduce subjectivity, and that multi-temporal snapshots fail to fully represent continuous phenological development, affecting classification precision. Therefore, analyzing denser S2 time series is a promising direction for future research. A time series refers to a sequence of remote-sensing images capturing the evolution of land surface characteristics over time. These images are processed to capture key phenological events in crop development. Denser Sentinel-2 (S2) time series offer clear advantages. [PERSON] et al. [9] demonstrated that dynamic thresholding can extract continuous phenological signals from S2 data. However, optical images are frequently interrupted by cloud cover. These gaps still hamper classification in persistently cloudy regions, indicating the need for multi-sensor fusion. Hence, combining S2 and S1 time-series data has emerged as an effective strategy to overcome the intrinsic limitations of single-sensor time series. [PERSON] et al. [10] demonstrated early-season crop monitoring by combining S1 and S2 time series, which improved reliability. However, the differing revisit intervals between S1 and S2 prevent perfect temporal alignment, leading to some limitations. To mitigate this, several studies have explored advanced fusion techniques. [PERSON] et al. [11] proposed an S1-S2 fusion method based on random forests, which effectively integrates SAR and optical features, significantly enhancing crop-type classification accuracy. [PERSON] [12] applied machine-learning-based temporal fusion of S1 and S2 data to improve vegetation mapping, particularly in heterogeneous regions, boosting the accuracy and robustness of vegetation monitoring. However, these fusion algorithms typically rely on manual feature selection and operate in high-dimensional feature spaces, resulting in high computational costs and limited adaptability to heterogeneous data sources. Moreover, such methods often struggle with data gaps or noise, and in large-scale applications, data quality and completeness are critical to fusion efficacy. Deep-learning techniques, with their ability to automatically extract features and model complex relationships, show great potential for handling high-dimensional, multi-source remote-sensing data. However, in the context of highly diverse non-food crops exhibiting substantial phenological variability, issues, such as noise, cloud obstruction, and data incompleteness, challenge the stability and generalization of deep-learning models. Despite these advances, three issues remain insufficiently addressed. First, Moderate-resolution sensors or single-date imagery struggle to resolve spectrally similar non-food crops whose phenological signals diverge only at brief, growth-critical stages. Secondly, existing deep-learning approaches seldom exploit crop-calendar knowledge; fixed temporal windows or feature averaging dilute the discriminative phases that truly separate oilseed rape, tea and cotton. Finally, Optical-only methods are vulnerable to cloud-induced gaps, while most fusion frameworks still treat radar and optical streams as independent modalities, leaving their complementary dynamics under-utilized. These gaps hinder large-scale, fine-grained mapping of diverse non-food crops and motivate the phenology-aware, multi-modal strategy proposed in this work. In addition, despite the advantages of deep-learning-based classification in capturing the diversity of non-food crops, its performance and robustness still require further validation and optimization.