Abstract. Over 90 % of the world’s rice is produced in the Asia-Pacific Region. Synthetic aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. Rice growth patterns in tropical and subtropical regions are complex, and it is difficult to construct representative rice growth patterns, which makes it much more difficult to extract rice distribution based on SAR data. To address this problem, a rice mapping method based on time-series Sentinel-1 SAR data is proposed in this study for large regional tropical or subtropical areas. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatio-temporal statistical features with thegeneralization ability to complex rice growth patterns was selected, then input into the U-Net semantic segmentation model and combined with WorldCover data to eliminate false alarms, and finally the 20-meter resolution rice map of five countries in mainland Southeast Asia in 2019 was obtained. On the validation sample set, the proposed method achieved an accuracy of 92.20 %. Good agreement was obtained when comparing our rice map with statistical data and other rice maps at the national and provincial levels. The maximum coefficient of determination R² was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice extraction with complex cropping patterns and the reliability of the generated rice maps. The 20 m annual paddy rice map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun, 2022).

中文翻译：

---

使用 Sentinel-1 SAR 数据绘制东南亚大陆 20 米年度水稻地图

摘要。世界上 90% 以上的稻米产自亚太地区。合成孔径雷达（SAR）可以对热带和亚热带地区的水稻分布进行全天候全天候观测。热带和亚热带地区水稻生长模式复杂，难以构建具有代表性的水稻生长模式，这使得基于SAR数据提取水稻分布变得更加困难。为了解决这个问题，本研究提出了一种基于时间序列 Sentinel-1 SAR 数据的大区域热带或亚热带水稻制图方法。在分析东南亚大陆水稻后向散射特征的基础上，选择了时空统计特征与对复杂水稻生长模式泛化能力相结合的方法，然后输入到U-Net语义分割模型中，结合WorldCover数据进行误报剔除，最终得到2019年东南亚大陆五国20米分辨率水稻地图。在验证样本集上，所提出的方法达到了 92.20% 的准确率。将我们的水稻地图与国家和省级的统计数据和其他水稻地图进行比较时，获得了很好的一致性。最大决定系数R 将我们的水稻地图与国家和省级的统计数据和其他水稻地图进行比较时，获得了很好的一致性。最大决定系数R 将我们的水稻地图与国家和省级的统计数据和其他水稻地图进行比较时，获得了很好的一致性。最大决定系数R²国家级为0.93，省级为0.97。这些结果证明了所提出的方法在具有复杂种植模式的水稻提取中的优势以及生成的水稻地图的可靠性。东南亚大陆每年 20 米的水稻地图可在 https://doi.org/10.5281/zenodo.7315076（2022 年星期日）获取。