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

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Abstract

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.

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Data availability

The SWSM dataset generated in this study is openly available at Zenodo. Two repositories are provided: https://doi.org/10.5281/zenodo.15262116 and https://doi.org/10.5281/zenodo.15250534.

Code availability

The custom script used to read the NetCDF files in this study is publicly hosted on GitHub at the repository address: https://github.com/weizeyang1997/SWSM.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (42271392), the Opening Foundation of Xi’an Key Laboratory of Territorial Spatial Information (3001023545016), and the Open Fund of the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources (NRMSSHR2022Y02, NRMSSHR2023Y03). We also gratefully acknowledge the QA4SM platform for providing critical soil moisture validation services; their efforts have greatly facilitated and enhanced the quality of our soil moisture research.

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Authors and Affiliations

  1. Faculty of Resources and Environmental Science, Hubei University, Wuhan, 430062, China

    Zeyang Wei, Lifei Wei, Qikai Lu & Shuang Tian

  2. Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan, 430062, China

    Zeyang Wei, Lifei Wei, Qikai Lu & Shuang Tian

  3. Hubei Spatial Planning Research Institute, Wuhan, China

    Ting Wang

  4. College of Geography and Environmental Science, Zhejiang Normal University, Jinhua, 321004, China

    Fei Zhang

  5. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China

    Yanfei Zhong

Authors

  1. Zeyang Wei
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Contributions

Z.W. and L.W. conceived the overall experiment; Z.W. conducted the entire experiment and wrote the manuscript; T.W. provided computational resources; Q.L. optimized the experimental procedure; S.T. assisted in data cleaning; F.Z. reviewed the manuscript; and Y.Z. guided the methodology and made revisions to the figures and manuscript. All authors have read and approved the final manuscript.

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Lifei Wei.

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Wei, Z., Wei, L., Wang, T. et al. A global long-term daily multilayer soil moisture dataset derived from machine learning.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06436-0

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