Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a henology domain ptical-AR feature inegration method to utomatically generate single (SC-Rice) and double cropping ice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

中文翻译：

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水稻种植模式测绘中集成物候域光学SAR特征的自动样本生成方法


水稻种植模式的准确时空信息对于预测粮食产量、管理水资源和评估温室气体排放至关重要。然而，目前区域尺度水稻种植模式的自动绘制受到训练样本不足和主要水稻产区频繁的阴天天气的严重限制。为了应对这一挑战，我们提出了一种汉学领域 ptical-AR 特征集成方法，可自动生成单季 (SC-Rice) 和双季冰 (DC-Rice) 样本 (POSTAR)，以实现高效、精细的水稻测绘。 POSTAR包括三个主要步骤：（1）使用基于物候和对象的光学数据分类方法（Sentinel-2 MSI）生成潜在的水稻图，以选择候选水稻样本； （2）根据独特的基于SAR（Sentinel-1 SAR）物候特征，采用K-means识别SC-和DC-Rice候选样本； (3)实施两步细化策略来过滤高置信度的SC-和DC-Rice样品，保持类内物候方差和样品纯度之间的平衡。选择验证的试验区域包括华南的洞庭湖平原和鄱阳湖平原以及华北三江平原的富锦县。事实证明，POSTAR 可有效生产可靠的 SC 和 DC 水稻样品，与现场样品实现高光谱相关相似性 (>0.85) 和低动态时间包裹距离 (<8.5)。将 POSTAR 衍生样本应用于随机森林分类器，总体准确率为 89.6%，在洞庭湖平原 SC-Rice 的 F1 分数为 0.899，DC-Rice 的 F1 分数为 0.938。 由于结合了基于知识的光学和SAR物候特征，POSTAR表现出很强的空间可迁移性，在鄱阳湖平原的总体精度达到96.0%，在富锦县的总体精度达到97.8%。这些结果证明了 POSTAR 方法无需进行大量实地考察即可准确绘制水稻种植模式的有效性，通过光学和 SAR 数据的集成为大面积、多样化和多云地区的作物监测提供了宝贵的见解。