## 4 Results
### Spatial Generalization Ability of the Inter-Continental Transferred Rice Mapping Model  
The rice mapping model fine-tuned based on the 2017 Shuangcheng dataset was further extended to three typical rice-growing areas in Northeast China, i.e., the Songnen, Liaohe, and Sanjiang plains (Figures 15-17), to test the spatial generalization ability of the inter-continental transferred model. The accuracy was validated using 1500 validation samples points from Northeast China, and the results showed that the overall accuracy of rice mapping reached 96.87%, the Kappa coefficient reached 0.8637, and the recall and precision of rice reached 0.8065 and 0.9722, respectively (Table 2). This result indicates that it is feasible to use a small amount of data from a small region to perform model inter-regional transfer and then apply the transferred model to a larger region for rice recognition, which helps to reduce the difficulty of large-scale inter-regional transfer and application of crop recognition models. However, we also observed that the recall value of rice is far lower than the precision value, indicating that the model is more prone to misclassifying rice pixels as non-rice pixels compared to misclassifying non-rice pixels as rice pixels. We further compared our rice mapping results with the crop distribution map produced by [PERSON] et al. [12]) to analyze the reason for this phenomenon. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ClassificationTruth** & **Paddy Rice (Points)** & **Others (Points)** & **Precision** \\ \hline Paddy rice (Points) & 175 & 5 & 0.9722 \\ Others (Points) & 42 & 1278 & 0.9682 \\ Recall & 0.8065 & 0.9961 & \\ \hline Overall accuracy & 96.87\% & Kappa coefficient & 0.8637 \\ F-score & 0.8816 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of rice maps in Northeast China in 2017. Figure 14: Time-series curves for the Sentinel-1 VV and VH of the main crops in the Shuangcheng area from 2017 to 2020. The blue and red curves represent the mean backscattering coefficients of rice and corn, respectively. The light-colored areas around the curves represent \(\pm 1\sigma\) from the average. Figure 16: Comparison between reference paddy rice map (**a**) and paddy rice map produced by fine-tuned attTFBS model (**b**) in the Sanjiang Plain (Region E) in 2017. Figure 15: Comparison between reference paddy rice map (**a**) and paddy rice map produced by fine-tuned attTFBS model (**b**) in the Songnen Plain (Region D) in 2017. Figure 18 illustrates the accuracy of the rice mapping results as compared to the crop maps created by [PERSON] et al. [12]. With respect to the overall accuracy of rice classification, as represented by the F-score and Kappa coefficient in the Songnen, Liaohe, and Sanjiang plains, the transferred model demonstrated high rice recognition accuracy in all three regions. The F-score values were 0.8697, 0.8625, and 0.8463, while the Kappa coefficients were 0.8605, 0.8537, and 0.8315, respectively. This indicates that the inter-continental transferred model has a robust ability for spatial generalization. Additionally, the precision values in all three regions (0.9192, 0.9001, and 0.9458) were noticeably higher than the recall values (0.8252, 0.8280, and 0.7657), suggesting that the omission error for rice pixels was more pronounced than the commission error. This phenomenon can also be observed intuitively in the Sankey diagrams of rice classification (Figure 19). The narrow light blue flows in Figure 19 indicate that a significant portion of rice pixels are misclassified as non-rice pixels in all three regions, while only a small number of non-rice pixels are misclassified as rice pixels.