## 6 Conclusions  
Aiming to address the conflict between the requirement for massive samples and the lack of sample data in the process of building a large-scale crop mapping model based on deep learning and remote sensing data, this paper proposes an inter-continental transfer method based on fine-tuning and realizes the large-scale high-resolution rice mapping based on a very small number of samples. The following conclusions were drawn:  
1. A mere 10 samples were used to achieve inter-continental transfer of the pre-trained model in this study, resulting in high-accuracy rice mapping of three typical regions in Northeast China, with an F-score of 0.8502.
2. With the continued increase in sample size, the accuracy improvement in the transfer model was not significant. When the sample size is 50, the F-score is 0.8560, which is almost the same as when the sample size is 10, indicating that the inter-continental transfer of the model does not require too many samples.
3. Transfer learning based on a small number of samples can achieve similar accuracy to models trained on a large number of samples, indicating that the method proposed in this paper maintains high accuracy in rice recognition while effectively reducing the sample requirement.
4. The transferred rice mapping model exhibits strong spatiotemporal generalization ability and can be used directly for rice mapping in multiple years and larger areas, which greatly reduces the workload of large-scale rice mapping, indicating high practical value of the proposed method.
5. The visualization results of the model features indicate that for the close-range transfer of the pre-trained model, it is only necessary to fine-tune the output layer's parameters, while for inter-continental transfer, the pre-trained model can no longer extract effective features for rice recognition, so it is necessary to synchronize adjust the parameters of the feature extraction layer of the pre-trained model.
6. The phenological differences of rice significantly affect the generalization ability of the transfer model. However, follow up research must further strengthen our knowledge on the influencing factors that affect the spatiotemporal generalization ability of the model to provide more powerful theoretical and practical support for the spatiotemporal transfer of the model.  
Conceptualization, L.Y., R.H. and J.H.; methodology, L.Y.; software, L.Y.; validation, L.Y., L.W. and J.D.; formal analysis, L.Y. and J.S.; investigation, L.Y.; resources, L.Y.; data curation, L.Y.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y. and J.Z.; visualization, L.Y.; supervision, J.H.; project administration, L.Y. and J.H.; funding acquisition, L.Y. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42201412 and 42171314, and the Youth Innovation Science and Technology Support of Universities in Shandong Province, grant number 2021 RW004.  
The code of the attTFBS model together with the dataset are available at: [[https://github.com/younglimpo/attTFBS](https://github.com/younglimpo/attTFBS)]([https://github.com/younglimpo/attTFBS](https://github.com/younglimpo/attTFBS)), (accessed on 12 September 2022).  
The authors would like to thank USDA-NASS for providing the CDL dataset. They also thank the anonymous reviewers for their constructive comments and advice.  
The authors declare no conflict of interest.