基于Sentinel-2的青铜峡灌区水稻和玉米种植分布早期识别
收稿日期: 2023-10-01
修回日期: 2023-12-28
网络出版日期: 2024-05-30
基金资助
国家自然科学基金项目(52209059);宁夏自然科学基金优秀青年项目(2023AAC05013);宁夏回族自治区重点研发计划项目(2021BBF02027)
Early identification of rice and corn planting distribution in Qingtongxia irrigation area based on Sentinel-2
Received date: 2023-10-01
Revised date: 2023-12-28
Online published: 2024-05-30
及时准确地掌握灌区内作物种植分布对于灌溉水资源高效配置、农田精准管理具有重要指导意义。以宁夏青铜峡灌区为研究对象，利用多时相Sentinel-2卫星数据，通过水稻和玉米早期特征分析，提取关键的“水淹”信号和“植被”信号，构建时序归一化差异水体指数（MNDWI）和归一化植被指数（NDVI）特征值数据集，并通过样本分析关键特征阈值，构建水稻和玉米早期种植分布决策树模型，提取2022年宁夏青铜峡灌区水稻和玉米种植的空间分布。结果表明:（1） 玉米和水稻苗期的后半段5月15—31日，水淹信号和植被信号是区分二者关键时期。（2） 基于早期作物物候特征的方法，在5月16—31日获取的水稻和玉米图像制图精度高于90%，用户精度超过91%，总体精度超过90%，Kappa系数高于0.88，明显高于同时期随机森林方法的分类精度。（3） 本研究提出的方法在早期水稻和玉米种植分布提取方面具有较强的适用性，并且能够在时空尺度上以较少的实地样本进行延展，同时在时间上也更有优势。因此，该方法为青铜峡灌区水稻和玉米种植分布早期调查提供了重要的方法支撑。
朱磊 , 王科 , 丁一民 , 孙振源 , 孙伯颜 . 基于Sentinel-2的青铜峡灌区水稻和玉米种植分布早期识别[J]. 干旱区地理, 2024 , 47(5) : 850 -860 . DOI: 10.12118/j.issn.1000-6060.2023.541
Timely and accurate understanding of crop distribution within irrigation areas is essential for the efficient allocation of irrigation water resources and precise field management. This study focuses on the Qingtongxia irrigation area in Ningxia, China, employing multitemporal Sentinel-2 satellite data to analyze early characteristics of rice and maize. Key “flooding” and “vegetation” signals are extracted, and a time-series dataset comprising the modified normalized difference water index (MNDWI) and normalized vegetation index (NDVI) is constructed. By analyzing sample thresholds for these key features, a decision tree model for the early planting distribution of rice and maize is established, facilitating the extraction of the spatial distribution for rice and maize planting in the Qingtongxia irrigation area in 2022. The results reveal the following: (1) During the latter half of the maize and rice seedling stages, from May 15 to 31, flooding and vegetation signals are crucial for differentiating between the two crops. (2) Based on the early crop phenological characteristics, the mapping accuracy of rice and corn images obtained from May 16 to May 31 was higher than 90%, with user accuracy exceeding 91% and overall accuracy exceeding 90%. The Kappa coefficient was higher than 0.88, significantly higher than the classification accuracy of the random forest classification method during the same period. (3) The proposed method demonstrates strong applicability in the early extraction of rice and maize planting distribution, requiring fewer ground samples for extension across both spatial and temporal scales. Therefore, this method provides significant support for early investigations of rice and maize planting distribution in the Qingtongxia irrigation area.
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