## 3 Methods  
Figure 2 shows the flowchart of this study, which included the following steps.  
1. Data preparation and preprocessing The time-series SAR images and corresponding label data used for model training and verification were cut into tiles with a spatial size of 128 \(\times\) 128 pixels. The time-series SAR image includes 18 bands; each band represents the VV or VH backscatter coefficient of a 24-day period.
2. Model pre-training A rice remote sensing recognition model was trained based on the dataset of the Mississippi Aluvial Plain (Region A, year 2019) in the southern United States with a total of 6608 samples to provide a pre-trained model for fine-tuning and transfer. Based on the Mississippi Aluvial Plain dataset, the TFBS and attTFBS models were trained and verified simultaneously by 10-fold cross-validation. The accuracies of the two models were compared; the model with the highest accuracy was used as the base model (Model A) for fine-tuning and transfer (Figure 3). Subsequently, the pre-trained model was directly used for rice mapping in the Shuangcheng District to test whether fine-tuning technique is needed to perform inter-continental transfer of the model.  
Model inter-continental transfer based on very few samples  
Model A was transferred and applied to the Shuangcheng District of Northeast China (Region C, Model C, 2017) by fine-tuning with a very small number of samples for rice classification and accuracy evaluation. The parameters of the first layer (temporal feature extraction layer) and the output layer of the pre-trained model were fine-tuned to achieve the inter-continental transfer of the model. In this study, the classification accuracy was evaluated by indicators, including the overall accuracy, F-score, recall, and precision.  
Spatial-temporal generalization ability test and impact factor analysis of the transferred model  
The model C was directly used for mapping rice in Shuangcheng (Region C) from 2018 to 2021, thereby testing the temporal generalization ability of the transferred model. In addition, the model was also used to produce the rice distribution map of Songnen (Region D), Liaohe (Region E), and Sanjiang Plain (Region F) in 2017, testing its spatial generalization ability. The accuracy of the classification results was evaluated using ground survey sample points and existing rice spatial distribution results, and factors affecting rice temporal and spatial generalization ability were analyzed.  
In addition, this study used the t-distributed stochastic neighbor embedding (t-SNE) feature visualization analysis method to analyze the differences between the short-distance transfer of the model (from the Mississippippi Alluvial Plain to the Sacramento Valley, Model B) and the inter-continental transfer of the model (from the Mississippippi Alluvial Plain to Northeast China, Model C), so as to analyze which network layer parameters need to be adjusted when using the fine-tuning method for model transfer.  
Evaluate the difference in crop recognition ability between the model transferred based on a small number of samples and model trained from scratch based on massive samples  
Model transfer can greatly reduce the dependence on sample data. However, the practicality of the proposed method may be reduced if there is a significant discrepancy in its rice recognition capabilities compared to those of a model trained from scratch using a large number of samples. Therefore, to evaluate the practicality of the proposed method, a deep learning-based rice mapping model was trained from scratch using all available data in Shuangcheng, Northeast China, and its performance was then compared with that of the transferred model in this study.  
Figure 3: Overall technical process used in this study.