Identifying spatial distribution of crop planting in large-scale is one of the most significant applications of remote sensing imagery. As an active remote sensing system, synthetic aperture radar (SAR) provides high-resolution polarimetric information of land covers. Nowadays, it is possible to carry out continuous multi-temporal analysis of crops in large-scales since an increased number of spaceborne SAR systems has been launched. This paper formulates rice mapping as a semantic segmentation problem and proposes to use deep learning techniques to exploit the phenological similarity of rice production to identify the rice distribution in large-scales. The study area (i.e., about 58504 km²) located in Arkansas River Basin is selected to develop an adapted U-Net for large-scale rice mapping. The Sentinel-1 data in previous years (i.e., data collected in 2017 and 2018) are used to train and fine-tune the network, and current season data (i.e., data collected in 2019) is selected to test the robustness of the network. Experimental results show that the proposed method achieves the state-of-the-art performance as it benefits from the spatial characteristics and phenological similarity of rice. The experiments of rice extraction in different planting pattern regions and extracted features visual projection are conducted to explain the features mined by the adapted U-Net. Furthermore, the advantages of temporal generalization in large-scale are validated by the comparison between space migration and time migration, which indicates that the difference of rice in different years is smaller than that of rice in different spaces. Finally, the issues for operational implementation are discussed.

大规模识别作物种植的空间分布是遥感影像最重要的应用之一。作为有源遥感系统，合成孔径雷达（SAR）可提供高分辨率的土地覆盖物极化信息。如今，由于已经启动了越来越多的星载SAR系统，因此有可能对作物进行连续的多时相分析。本文将水稻映射表述为语义分割问题，并建议使用深度学习技术来利用水稻生产的物候相似性来大规模识别水稻分布。研究区域（即约58504  km ²选择位于阿肯色河流域的）开发适用于大规模水稻制图的U-Net。前几年的Sentinel-1数据（即2017年和2018年收集的数据）用于训练和微调网络，选择当前季节的数据（即2019年收集的数据）来测试网络的健壮性。实验结果表明，该方法由于具有水稻的空间特征和物候相似性，因此具有最先进的性能。进行了不同种植模式区域的水稻提取和提取特征视觉投影的实验，以说明通过改编的U-Net提取的特征。此外，通过将空间迁移和时间迁移进行比较，可以验证大规模时间泛化的优势，这表明不同年份水稻的差异小于不同空间水稻的差异。最后，讨论了运营实施中的问题。