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Updated: Sep 16, 2025

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In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
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SMRFR:使用Random Forest从多来源数据中生成的全球多层土壤湿度数据集
Yuhan Liu1, Yuanyuan Zha2, Gulin Ran1
1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
Scientific data
|July 9, 2025
概括
一个新的机器学习框架,Soil Moisture via Random Forest Regression (SMRFR),可以生成一个全球性的多层土壤湿度数据集. 这一数据集准确地捕捉了土壤湿度动态,以改善农业和气候研究.
科学领域:
- 地球科学 地球科学 地球科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的土壤湿度监测对于农业,水文和气候建模至关重要.
- 现有的数据集往往缺乏连续,多层或全球覆盖.
研究的目的:
- 开发一种新的机器学习框架,即SMRFR (Soil Moisture via Random Forest Regression),用于生成持续更新的多层全球土壤湿度数据集.
- 提供每天的土壤湿度估计在五个土壤层 (0-100厘米) 的9公里空间分辨率从2000-2023.
主要方法:
- 利用公开可用的再分析和遥感数据.
- 采用随机森林回归模型来估计土壤水分.
- 验证了模型的空间和时间可变性,概括能力和准确性.
主要成果:
- SMRFR有效地捕捉了空间和时间的土壤湿度变化.
- 在各大洲表现出强烈的泛化,并准确地捕捉了降雨引起的动态.
- 在验证集上实现了0.0339 m3/m3的无偏根平均平方误差.
结论:
- SMRFR数据集为土壤水分分析提供了有价值的,不断更新的参考资料.
- 能够改善区域到全球范围的土壤湿度动态建模.
- 支持农业,水文和生态研究的进步.
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