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一个全球长期的每日多层土壤湿度数据集,来源于机器学习.
Zeyang Wei1,2, Lifei Wei3,4, Ting Wang5
1Faculty of Resources and Environmental Science, Hubei University, Wuhan, 430062, China.
Scientific data
|December 15, 2025
概括
一个新的全球土壤湿度数据集 (SWSM) 每天提供深层土壤层的高分辨率估计. 这种宝贵的资源有助于水文建模和农业水资源管理.
科学领域:
- 地球科学 地球科学 地球科学
- 水文学的水文学
- 数据科学数据科学数据科学
背景情况:
- 土壤水分对于地球的能量和水循环至关重要.
- 现有的土壤湿度产品往往缺乏对更深层土壤的连续,高分辨率数据.
研究的目的:
- 为了生成一个全球,每日,无的多层土壤湿度数据集 (SWSM),涵盖2002-2021年.
- 以0.05°的空间分辨率提供三个不同的土壤深度地平线 (0-10厘米,10-30厘米,30-60厘米) 的估计.
主要方法:
- 使用机器学习方法 (XGBoost) 创建SWSM数据集.
- 利用现场观测来严格验证生成的数据.
主要成果:
- 在所有深度上,SWSM数据集显示出高精度,Pearson相关系数>0.90和RMSE<0.05.
- 特性重要性分析证实了数据集的物理一致性和深度依赖的水文模式.
结论:
- SWSM数据集提供长期时间覆盖,精细的空间分辨率和多层结构.
- 这一数据集是水文建模,农业水资源管理和气候变化研究的宝贵资源.
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