## 4 Results
### Rice Mapping in Northeast China Based on the Pre-Trained Model without Fine-Tuning  
We then used the model (Model A) trained in the Mississippi Aluvial Plain (Region A) directly for rice mapping in the Shuangcheng area of Northeast China (Region C) in 2017, and the classification accuracies are shown in Table 1. From Table 1, it is clear that the model without fine-tuning had difficulty mapping rice in the target area, and its F-score of rice was only 0.2253, and the Kappa coefficient was only 0.1895. It should be pointed out that although the overall accuracy of classification has reached 90.31%, the proportion of rice pixels in the total number of pixels in the Shuangcheng area is only 2.87%. Therefore, the overall accuracy cannot represent the true classification accuracy of the model for rice, which can also be seen from the recall and precision values of rice in Table 1. Specifically, the recall value of the pre-trained model for rice in the Shuangcheng area was only 0.4916, indicating that up to 50.84% of rice pixels have been misclassified; The precision value of rice was only 0.1462, indicating that up to 85.38% of rice pixels in the classification result were actually pixels of other classes. To investigate the reasons for this result, we compared the differences in image data and rice features between the United States and China. Figure 7a,c are pseudo-color images of raw SAR data from the United States (Region A) and Northeast China (Region C), respectively. Figure 7b,d are pseudo-color PC images of the United States (Region A) and Northeast China (Region C), respectively. The white polygons in Figure 7 represent rice fields. Figure 7 shows a significant difference in color between the rice fields in the United States and Northeast China, indicating that the rice fields exhibit different temporal features in the images of the two regions. Therefore, we calculated the SAR time-series curves of rice and corn in the two regions, as shown in Figure 8. Figure 8 indicates that the minimum VV value of rice in Shuangcheng appeared on 19 May, which was significantly later than that of Mississippi Aluvial Plain (25 April). In addition, we further made the probability density functions (PDFs) and boxplots of the backscattering coefficient values of rice pixels in all 9 periods of Region A and Region C (Figures 9 and 10). Figures 9 and 10 indicate that there were significant differences in the distribution of rice backscatter coefficient values between Region A and Region C on 1 April, 25 April, 19 September, and 10 October. Taking 25 April as an example, the peak value of the PDF of the rice pixels in Region A appears at \(-\)14.14, its standard deviation is 3.24 (Figure 9b), its median is \(-\)13.94, and the interquartile range (IQR) is 4.6 (Figure 10b); The peak of the PDF of the rice in Region B appears at \(-\)10.99, with a standard deviation of 2.27 (Figure 9b), a median of \(-\)11.27, and an IQR of 2.8 (Figure 10b). 0.3474, 0.0992, and 0.3553, respectively. When the sample size was increased to 10, the accuracy of model fine-tuning and transfer reached a relatively high level; the average F-score, kappa coefficient, recall, and precision of rice were 0.8502, 0.8439, 0.8345, and 0.8669, respectively, with standard deviations of 0.0030, 0.0032, 0.0108, and 0.0115, respectively. However, as the number of samples continued to increase, the accuracy of the model transfer increased slowly, whereas the stability of the model remained basically unchanged. When the sample size was increased to 50, the average F-score, kappa coefficient, recall, and precision of rice were 0.8560, 0.8499, 0.8424, and 0.8704, respectively, with standard deviations of 0.0029, 0.0029, 0.0135, and 0.0107, respectively. Based on these results, the number of samples for model inter-continental transfer was set to 10 to reduce the demand of samples to the greatest extent possible on the premise of ensuring high model transfer accuracy. Figure 7: Comparison of Sentinel-1 SAR images in the source (Region A) and target regions (Region C).