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<p>Soil moisture is a critical component of the Earth’s energy and water cycles. However, most existing products focus solely on surface layers, and continuous, high‐resolution datasets for deep soil horizons remain scarce. To address this gap, we generated a global, daily, seamless multilayer soil moisture dataset (SWSM) for the period 2002–2021 by leveraging a machine learning approach (XGBoost). The SWSM dataset provides estimates at a 0.05° spatial resolution for three depth horizons: 0–10 cm, 10–30 cm, and 30–60 cm. Rigorous validation against <i>in situ</i> observations demonstrated the dataset’s high accuracy, with Pearson correlation coefficients exceeding 0.90 and root mean square errors below 0.05 across all depths. A feature importance assessment verified the dataset’s physical consistency, revealing depth-dependent patterns aligned with established hydrological understanding. The SWSM dataset, with its long-term temporal coverage, fine spatial resolution, and multi-layer structure, is a valuable resource for applications in hydrologic modeling, agricultural water management, and climate change studies.</p>

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A global long-term daily multilayer soil moisture dataset derived from machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

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  • Zeyang Wei,
  • Lifei Wei,
  • Ting Wang,
  • Qikai Lu,
  • Shuang Tian,
  • Fei Zhang,
  • Yanfei Zhong

摘要

Soil moisture is a critical component of the Earth’s energy and water cycles. However, most existing products focus solely on surface layers, and continuous, high‐resolution datasets for deep soil horizons remain scarce. To address this gap, we generated a global, daily, seamless multilayer soil moisture dataset (SWSM) for the period 2002–2021 by leveraging a machine learning approach (XGBoost). The SWSM dataset provides estimates at a 0.05° spatial resolution for three depth horizons: 0–10 cm, 10–30 cm, and 30–60 cm. Rigorous validation against in situ observations demonstrated the dataset’s high accuracy, with Pearson correlation coefficients exceeding 0.90 and root mean square errors below 0.05 across all depths. A feature importance assessment verified the dataset’s physical consistency, revealing depth-dependent patterns aligned with established hydrological understanding. The SWSM dataset, with its long-term temporal coverage, fine spatial resolution, and multi-layer structure, is a valuable resource for applications in hydrologic modeling, agricultural water management, and climate change studies.