## 4 Results
### Rice Mapping in Northeast China Based on the Pre-Trained Model without Fine-Tuning
(**a**) Pseudo-color SAR image of Region A (R: 1 April; G: 25 April; B: 19 May). (**b**) Pseudo-color PC image of Region A (R: PC 1; G: PC 2; B: PC 3). (**c**) Pseudo-color SAR image of Region C (R: 01 April; G: 25 April; B: 19 May). (**d**) Pseudo-color PC image of Region C (R: PC 1; G: PC 2; B: PC 3). Figure 8: The comparison of Sentinel-1 VV time-series curves of rice and corn in Shuangcheng (Region C) with those in the Mississippi Althuvial Plain (Region A). Figure 9: Probability density functions (PDFs) of VV backscattering coefficients of rice pixels in all 9 time periods of SAR images in the Mississippi Alluvial Plain of the United States and the Shuangcheng District of China. Rice Mapping of the Shuangheng District Based on the Fine-Tuned Model and Its Temporal Generalization Ability  
Based on the fine-tuned rice classification model, rice mapping in the Shuangcheng area from 2017 to 2020 was conducted (Figure 12). The accuracy of the rice classification results was evaluated based on reference maps (Figure 13). The results showed that it is feasible to realize the inter-continental transfer of a pre-trained rice mapping model based on a fine-tuning method and a small sample size, which is of great significance for rice mapping in areas lacking samples and can significantly reduce the cost of deep learning-based rice mapping models. The F-score values of the rice maps for 2018, 2019, and 2020 reached 0.8316, 0.8180, and 0.8432, respectively. The kappa coefficients of the rice maps for 2018, 2019, and 2020 were 0.8259, 0.8123, and 0.8388, respectively (Figure 13). The high F-score and kappa values indicated that the model after inter-continental transfer based on  
Figure 10: Boxplots of VV backscatter coefficients of rice pixels in all 9 periods of SAR images of Mississippi Aluvial Plain and Shuangcheng District. The top and bottom sides of the box are the lower and upper quartiles. The box covers the interquartile interval (interquartile range, IQR), where 50% of the data are found. The horizontal line that splits the box in two is the median. The top and bottom whiskers are two lines outside the box, which represent maximum and minimum of the data. a small number of samples still had a strong temporal generalization ability, which further expands the applicability of the model after transfer. The precision values of the rice maps in 2018, 2019, and 2020 reached 0.9154, 0.9604, and 0.9402, respectively (Figure 13), which indicated that the pixels of other ground object classes were barely misclassified into rice when the transferred model was applied over an extended period. Moreover, the recall values of the rice maps in 2018, 2019, and 2020 were 0.7619, 0.7124, and 0.7644, respectively, which were significantly lower than the precision values, indicating that, compared with the commission error for rice, the transferred model was more prone to rice omission errors (i.e., misclassifying rice pixels into pixels of other classes). This was due to the slightly different values of the rice backscattering coefficient between different years in the Shuangcheng area, which reduced the possibility of rice pixels being identified and led to a lower recall value (Figure 14). For example, the VV values of rice in 2017 decreased from \(-11.2\) (on 25 April) to \(-14.9\) (on 19 May) during the transplanting stage, with a decrease of 3.7, indicating that rice pixels in 2017 showed significant irrigation characteristics during the transplanting stage. However, in 2018, 2019, and 2020, the VV value only decreased by 1.8, 2.4, and 2.3, respectively, during the transplanting stage, which was significantly smaller than that in 2017, indicating that the irrigation characteristics of paddy fields were not as significant as those in 2017, thus leading to the omission of rice classification (Figure 14).