## 6 Discussion
### Rapid Changes in Spectral Features and Missing Temporal Data in Non-Food Crop Growth Stages  
Non-food crop phenological changes are accompanied by marked physiological and morphological transformations that directly influence their spectral signatures. Throughout development--from emergence to maturity--variations in chlorophyll content, leaf structure, and pigmentation induce pronounced fluctuations in reflectance, particularly in the red-edge and near-infrared bands. For example, during the flowering stage, abrupt changes in chlorophyll concentration lead to a sharp increase in near-infrared reflectance, whereas the senescence processes of maturity produce even more conspicuous spectral shifts [38]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Precision (\%)**} & \multicolumn{3}{c}{**HN-NG Set**} & \multirow{2}{*}{**mPA (\%)**} \\  & & & **Recall (\%)** & & **F1\_Score (\%)** & **IoU (\%)** \\ \hline PVM & 82.16 & 80.04 & 74.84 & 61.38 & 70.46 \\ MTAF-TST & 80.25 & 77.94 & 72.68 & 58.44 & 67.82 \\
2 DU-Net+CLSTM & 77.83 & 74.88 & 69.36 & 54.79 & 65.40 \\ PVM (U-Net) & 79.66 & 77.12 & 71.25 & 56.87 & 68.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of PVM and mainstream models (HN-NG Set). When the temporal resolution of remote-sensing data is insufficient, these rapid spectral dynamics--especially at critical phenological windows, such as antiresis or ripening--may not be captured, thereby degrading the model's ability to recognize and classify non-food crops accurately [39; 40]. Although our spatiotemporal fusion algorithm (e.g., MTAF) partially mitigates the temporal sparsity of Sentinel-2 data caused by cloud and rainfall interruptions by operating directly on the original, non-interpolated time series--thus preserving authentic observation discontinuities--it still risks missing key phenological observations. In periods of prolonged cloud cover, critical Sentinel-2 acquisitions may be entirely unavailable, impairing the model's capacity to learn precise temporal patterns and semantic distinctions. Empirical studies have demonstrated that such time-series gaps, particularly in optical imagery obstructed by clouds, significantly undermine classification performance for non-food crops [41; 42; 43]. To address this, recent research has proposed multi-source data supplementation--combining Sentinel-3 with Sentinel-2 observations--to impute missing temporal segments, thereby restoring series completeness and enhancing the model's fidelity in capturing crop growth dynamics during abrupt phenological transitions, ultimately improving overall accuracy and robustness [44]. Environmental Heterogeneity and Non-Food Crop Phenology Changes: Challenges and Prospects for Adaptive Models  
Phenological changes in non-food crops are influenced by a variety of environmental factors, especially climate, soil, and terrain heterogeneity. This environmental heterogeneity leads to significant phenological dynamic changes in non-food crops across different regions and growth stages. For instance, in areas with large differences in climate and soil conditions, the same non-food crop may have different growth rhythms and time points for key growth stages. In this context, static phenological assumptions often fail to adapt to dynamic changes in the local environment, leading to model uncertainty and errors [45]. Static phenological assumptions typically assume that the changes in non-food crop phenology follow fixed patterns, ignoring the impact of climate fluctuations and soil differences. For example, in some areas, the growth stages of non-food crops may be advanced or delayed, thus affecting the changes in spectral features, making it difficult for the model to accurately recognize the key growth stages of non-food crops [46]. Moreover, crop rotation can also cause temporal signal variations.