从1982年到2015年使用多源数据和机器学习以5分钟的分辨率绘制四种主要作物的全球产量
Juan Cao1,2, Zhao Zhang3, Xiangzhong Luo4
1School of National Safety and Emergency Management, Beijing Normal University, Beijing, 100875, China.
Scientific data
|February 28, 2025
概括
一个新的数据集,GlobalCropYield5min,提供了高分辨率的1982-2015年全球作物产量数据. 该资源通过提供准确,连续的作物产量信息来增强粮食安全分析和农业风险管理.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 全球粮食安全依赖于准确的作物产量数据,但目前的数据集缺乏足够的空间和时间细节.
- 现有的全球收益率数据集在解决方案中存在局限性,阻碍了全面的风险评估.
研究的目的:
- 推出GlobalCropYield5min,这是一个新的高分辨率网格数据集,用于从1982年到2015年的主要作物产量 (玉米,大米,小麦,大豆).
- 提高全球作物产量数据的准确性和时空覆盖率,以加强食品系统风险评估.
主要方法:
- 开发了每个国家和作物的三种机器学习 (ML) 模型,将行政作物统计与卫星数据,气候变量,土壤特性,农业实践和气候模式相结合.
- 选择了最佳预测因素和ML模型,以以5弧分钟的空间分辨率 (约为5x5弧分钟) 估计年度作物产量.
主要成果:
- 实现了强大的模型性能,R平方值从0.70到0.95和正常化根平均平方误差 (NRMSE) 从5%到20%不等.
- 与现有的全球产量数据集相比,GlobalCropYield5min显示出优越的空间分辨率,时间覆盖和准确性.
结论:
- 全球作物收益率5分钟数据集为详细分析气候和作物收益率相互作用提供了宝贵的资源.
- 这一数据集对于改善农业灾害风险管理和了解全球粮食系统动态至关重要.
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