## 5 Discussion
### Proposed Pa-PCA-Ca Structure  
The proposed ensemble framework achieved higher OA and Kappa accuracies compared to conventional approaches that utilize a simple stacking of FC for classification, as shown in Figure 11. This is due to the fact that conventional approaches, which primarily rely on a single ML model, often suffer from redundancy and correlation among input features. This redundancy and correlation between input features leads to the occurrence of the Hughes Phenomenon, resulting in a deterioration of the performance of ML models [34]. Even when PCA is implemented in conventional approaches to address this issue, the proposed method of this article still achieved superior accuracies. The proposed method with an OA of 96.25% also demonstrated state-of-the-art performance in the existing literature. For instance, a novel multi-feature ensemble method based on SVM and RF was developed in [10] which achieved an OA of 90.96%. Convolutional Neural Networks in [11] and [38] also achieved OAs of 91.6%, and 94.6%, respectively. An iterative RF model in [14] also achieved a maximum OA of 89.81%. The adaptive stacking of ML models in [44] also achieved an OA of 88.53%. The superior performance of the proposed ensemble structure can be attributed to several reasons, described below. Firstly, the Pa part consisted of five parallel branches, each corresponding to a specific FC (FC-1-5 in Table 3). These parallel branches generate PMs for each target class, providing a multi-view representation of the data. This allows the model to leverage the complementary information present in each FC [54]. Additionally, it enables the model to potentially capture a broader range of patterns and characteristics relevant to the classification problem [57]. This finding is supported by the results in Figure 6, where the simultaneous use of the five FCs as five distinct branches in the Pa structure yielded the highest accuracy compared to other scenarios. By utilizing multi-source data, the number of satellite observations per cropland increases, providing more information about crops [21]. In other words, multi-source data contain different aspects of crop types, including spectral, phenological, physical, and structural characteristics [2; 14]. All of these advantages enhance the model's ability to discriminate between different classes and improve the classification accuracy. The improvement in classification accuracy by combining MS and SAR time series has also been reported in other studies [23; 24; 38; 67]. Secondly, the classifier in the Ca structure directly identifies the target classes from the output PMs generated by the Pa part. As depicted in Figure 7, the output PMs from the Pa part were highly correlated in each class. Therefore, it is crucial to select an optimal set of PMs that effectively represents the entire PM [47]. In this regard, PCA was employed to reduce data redundancy while preserving the most relevant information [45]. PCA identifies the directions in the PMs where the data exhibit the most variation, known as principal components. By selecting the top 'n' components (Table 6), a significant portion of the original PMs can be retained while reducing dimensionality. This approach differs from feature selection techniques that may not capture potentially useful information from the entire set of features [48]. Furthermore, by utilizing PCA, complex relationships and interactions among the features can be captured, which may not be achievable with simple feature selection techniques [75]. The incorporation of PCA before the Ca structure significantly reduced uncertainty in classification, as illustrated in Figure 9. This led to a substantial increase in classification accuracy (Figure 11). Thirdly, the Meta-model within the Ca structure utilized the collective knowledge from the parallel branches in Pa structure to make the final classification decision.