## 4 Discussion  
### Advantage of the Ensemble Model  
Timely and accurate crop spatial distribution and yield estimation monitoring are significant to national food security and strategic arrangements [55]. Deep learning algorithms  
Figure 14: (**A**) A municipal-level comparison of (**1**) wheat, (**2**) corn, and (**3**) rice cultivation areas detected from the MODIS data in 2005, 2010, 2015, and 2020 combining the PB-CB algorithm. (**B**) County-level comparisons of (**1**) wheat, (**2**) corn, and (**3**) rice planted detected from the MODIS data in 2015 combining the PB-CB algorithm. capable of mapping multiple crops' spatial distribution have proven effective in recent years [15; 17]. However, most methods for extracting multiple-crop data in North China have lower accuracy. [PERSON] et al. [56] used sparse-representation-based algorithms combined with the Leaf Area Index (LAI) to classify multiple crops in North China, and the final accuracy reached 76.05%. [PERSON] et al. [17] combined NaE and Stacking (SVM, RF, KNN) for multiple-crop classification in North China, achieving an overall accuracy of 77.12%. Mapping the crop area in regions with a large and multiple-crop type remains challenging. The development of a method capable of identifying multiple-crop types within a large geographical area is in demand. To this end, in this study a new PB-CB network is proposed, combining it with time series NDVI and EVI (NaE) data to generate crop planting distribution maps. The results show that PB-CB can effectively conduct crop classification research. Compared to existing methods, the PB-CB method offers several advantages. Firstly, deep learning models follow the end-to-end principle without needing expert experience and prior knowledge, which can automatically obtain crop results [20]. Secondly, Snapshot and SWA strategies combined with PB-Conv1D and PB-BiLSTM models can simplify the feature extraction process and obtain higher accuracy. Finally, combining multiple models is equivalent to fusing numerous features, resulting in better feature results, which is particularly well-suited for crop classification research. To better demonstrate the advantages of the PB-CB model, we conducted a comparison between the PB-CB model and two other models: the PB-CDSA classification model, which demonstrated the best performance in the single model, and the PB-BV model, which achieved the highest performance among the single model ensemble. Table 9 shows the difference between the confusion matrices of the PB-CB and PB-CDSA models, with positive values on the diagonal and negative values on the non-diagonal, indicating the number of samples correctly classified by the PB-CB model than PB-CDSA. The total showed that the PB-CDSA model outperformed PB-CB only in classifying SP. The PB-CB model indicated more vital classification ability when dividing rotation crops, especially WT-RI and WT-SU. By calculation, the number of samples correctly classified by the PB-CB than the PB-CDSA model accounted for 4.15% of the total test samples, which is enough to illustrate the advantages of the PB-CB model. Table 10 shows the difference between the confusion matrix of the PB-CB model and that of the PB-BV model, and the meaning is consistent with Table 7. The total indicates that the PB-BV model only showed a stronger classification ability than PB-CB when classifying WT-OT. This is because the PB-BV model had a better effect on WT-OT, while the PB-CB model integrates the PB-BV and PB-CV models, and the PB-CV model had a weak classification ability for WT-OT than integrated PB-BV and PB-CV, which reduced the classification ability of the PB-BV model. The two models exhibit similar classification capabilities for SP and OT. When dividing WT-SU, the PB-CB model showed a strong