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Updated: Feb 5, 2026

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从2016年到2021年在中国东北部的10m玉米,大米和大豆产量数据集
Fei Teng1,2, Minglei Wang1,3, Wenjiao Shi4,5
1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Scientific data
|February 3, 2026
概括
这项研究引入了一种用于中国东北部高分辨率作物产量测绘的新方法,为玉米,大米和大豆创造了有价值的数据集. 这种创新方法增强了农业决策和资源管理.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 准确的作物产量数据对于农业管理和资源分配至关重要.
- 现有的数据集缺乏高分辨率,长期的空间和时间作物产量信息.
- 解决这个差距对于优化农业实践至关重要.
研究的目的:
- 开发一种创新的方法来创建一个高分辨率的长期作物产量数据集.
- 绘制中国东北部玉米,大米和大豆产量的空间和时间分布图.
- 为农业决策提供有价值的资源.
主要方法:
- 开发了一种植被光合作用模型,与过渡系数集成.
- 引入了一个动态观测指数 (APARεg) 和一个复合收益率转换系数 (a).
- 创建了2016-2021年中国东北地区玉米,大米和大豆10m分辨率的数据集.
主要成果:
- 取得的验证确定系数 (r) 为玉米0.39,大米0.51,大豆0.52.
- 报告的平均相对误差 (MREs) 为12.14% (玉米),11.96% (大米) 和14.06% (大豆).
- 获得的根平均平方误差 (RMSE) 为16.97% (玉米),16.12% (大米) 和17.26% (大豆).
结论:
- 开发的方法提供了一种创新的方法,可以在不进行现场测量的情况下估计作物产量.
- 由此产生的高分辨率数据集为作物产量分布提供了重要的见解.
- 这一数据集支持改善农业决策和优化资源分配.
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