Department of Remote Sensing Imagery, Provincial Geomatics Center of Jiangsu, Nanjing, 210013, People's Republic of China.
Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210013, People's Republic of China.
Sci Rep. 2024 Aug 2;14(1):17886. doi: 10.1038/s41598-024-68960-0.
The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky-Golay (S-G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S-G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.
精确提取冬小麦种植结构对于粮食安全风险评估、农业资源管理和政府决策具有重要意义。本研究提出了一种考虑冬小麦生长物候学的冬小麦种植结构提取方法。利用拟合效果指数，自动确定最优的 Savitzky-Golay（S-G）滤波参数组合，实现 NDVI 时间序列数据的自动滤波和重建。利用阈值法自动识别冬小麦生长的物候期，然后基于三个关键物候期（播种期、抽穗期和收获期），结合层次分类原则，构建了冬小麦种植结构提取模型。利用历史冬小麦分布数据构建先验样本库，验证提取结果的准确性。不同地表拟合效果的验证表明，利用拟合效果指数可以自动获得 S-G 滤波的最优滤波参数。提取的冬小麦物候期与地面观测结果和 MOD12Q2 物候产品具有良好的一致性。与统计年鉴数据和提出的先验知识库的验证表明，该方法具有较高的统计精度和空间精度，提取精度为 94.92%，空间定位精度为 93.26%，kappa 系数为 0.9228。结果表明，该方法能够快速、显著地识别冬小麦种植区。此外，该方法不需要训练样本或人工经验，具有较强的可转移性。
