从作物统计到年度地图:通过机器学习和统计分类来跟踪特定作物区域的时空动态
Xiyu Li1, Le Yu2,3,4, Zhenrong Du5
1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
Scientific data
|July 16, 2025
概括
本研究提出了一个新的框架,用于创建每年特定作物面积地图,分辨率为10公里. 这些详细的地图有助于理解农业系统,并支持粮食安全评估.
科学领域:
- 农业科学 农业科学
- 地理空间分析的研究.
- 环境科学 环境科学
背景情况:
- 准确的作物面积的时空地图对农业系统管理至关重要.
- 缺乏可比的多阶段作物地图阻碍了全球农业分析.
- 现有的数据限制需要新的方法来详细地图作物.
研究的目的:
- 制定一个框架,以10公里分辨率更新年度特定作物面积地图.
- 整合多源数据和机器学习,以改进作物映射.
- 为农业和环境评估提供历史数据集.
主要方法:
- 使用作物统计数据的分类和多源数据的整合.
- 使用机器学习,特别是随机森林回归,带有空间指标.
- 开发了一个概率层来分解统计数据,并使用多个约束来协调数据.
主要成果:
- 为非洲42种作物种类 (1961-2022) 生成年度作物特定面积地图.
- 在中国 (1980-2022) 制作了14种作物类型的年度特定作物面积地图.
- 经过验证的产品与独立的参考数据显示出合理的一致性.
结论:
- 开发的框架成功生成了高分辨率,多时间作物地图.
- 这些作物地图为粮食安全和环境影响评估提供了有价值的数据基础.
- 该方法提供了一个可扩展的解决方案,用于在全球范围内绘制特定作物面积的地图.
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