## 4 Results and Discussion  
### Results  
#### 4.1.1 Results in Dijon  
Crop classification maps and local details generated using the proposed XGB-FR method and comparison approaches are presented in Figure 5. Limits to the manual interpretation of multi-temporal images make it difficult to evaluate classification performance through visual interpretations qualitatively. Nonetheless, overall performance can be evaluated in local regions. _'Grass'_ is mainly distributed along the riverbanks of the _Vingeaune_ (local region A in Figure 5) and to the southwest of the study area (Figure 5). _'Grape'_ is mainly cultivated in the narrow valley from _Chenove_ to _Beaune_ (local region B in Figure 5). The flatlands in the east-central region were covered by _'winter wheat'_, _'winter barley'_, _'corn'_, and so on. Table 2 provides a summary of the classification accuracies (OA, Precision, Recall, and F1 for the Dijon study area) generated from five comparison approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **XGB-Clean** & **LSTM-TS** & **XGB-TS** & **LSTM-FR** & **XGB-FR** \\ \hline OA & 76.92\% & 80.95\% & 79.81\% & 83.94\% & 84.52\% \\ Precision & 56.14\% & 65.61\% & 63.33\% & 72.23\% & 73.39\% \\ Recall & 65.93\% & 75.94\% & 72.07\% & 77.16\% & 77.81\% \\ F1 & 0.6064 & 0.7040 & 0.6741 & 0.7461 & 0.7553 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification accuracies in the Dijon study area (underline indicates the best scores). Overall, XGB-FR and LSTM-FR classifications employing feature representation performed significantly better than other classifications, with OA scores above 83.00%, precision scores exceeding 72.00%, recall scores exceeding 77.00%, and F1 scores exceeding 0.74, respectively. The XGB-TS and LSTM-TS classifications using raw time-series spectral features were followed, with OA scores of about 80.00%, precision scores of approximately 64.00%, recall scores of approximately 74.00%, and F1 scores of approximately 0.68, respectively. This indicates that the proposed contrastive learning framework could explore the inherent time-series features of crop growth to increase classification accuracy. With an OA of 76.92%, a precision of 56.14%, a recall of 65.93%, and an F1 score of 0.61, the XGB-Clean classification utilizing only clean images produced the worst results. This was expected given that these clean images are time-series spare. It is difficult to capture the essential phenological characteristics for classification, especially in July when crops grow rapidly. Figure 5: _Cont._  
Figure 5: Results of parcel-based crop mapping using comparative methods in the Dijon study area. (**a**) presents the whole crop classification using the XGB-FR method, (**b**,**c**) present the local details of crop classification from five comparison methods. The LSTM-TS classification performed marginally better than the XGB-TS classification when employing raw time-series spectral data. The possible reasons behind this are that LSTM models capture the long- and short-term dependence in the raw time-series observation, which is essential for crop identification. While using time-series feature representation, the XGB-FR classification surpassed the LSTM-FR classification. On the one hand, the proposed contrastive learning framework can explore the multi-scale dependencies from raw spectral features and express them in generated feature representation. On the other hand, XGBoost classifiers applied boost tree rules to feature representation and improve crop classification further. In other words, the best crop classification was made possible by the combined advantages of contrastive-learning-based representation and XGBoost-based classifiers.