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Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning
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  • Published: 12 February 2026

Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning

  • Sijia Feng1 na1,
  • Aoyang Li2,3,4,5 na1,
  • Rui Zhou  ORCID: orcid.org/0009-0005-2579-792X2,3,4,5,
  • Klaus Butterbach-Bahl1,6,
  • Kaiyu Guan2,3,4,5,
  • Zhenong Jin  ORCID: orcid.org/0000-0002-1252-25147,
  • Majken C. Looms8,
  • Sherrie Wang9,10,
  • Christian Igel11,12,
  • Claire Treat  ORCID: orcid.org/0000-0002-1225-81781,
  • Jørgen Eivind Olesen  ORCID: orcid.org/0000-0002-6639-127313 &
  • …
  • Sheng Wang  ORCID: orcid.org/0000-0003-3385-31091,2 

Scientific Data , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Hydrology

Abstract

Accurate estimation of surface soil moisture (SM) in terrestrial ecosystems is essential for understanding hydroclimate dynamics. The L-band Soil Moisture Active Passive (SMAP) mission provides 9-km global daily surface SM by using a microwave radiative transfer model (RTM)-based algorithm. However, the accuracy of SMAP SM is limited in regions with dense vegetation cover and complex surface conditions, due to the empirical parameterization and oversimplified radiative transfer processes. To overcome the limitations, we developed a Process-Guided Machine Learning (PGML) framework to integrate RTM theories and deep learning to predict global daily surface 9-km SM from April 2015 to June 2025. Informed by domain knowledge, we developed the PGML model structure using RTM and hydrological theories, designed a Kling-Gupta efficiency-based cost function, pretrained it with RTM simulations, and fine-tuned it with in-situ measurements. The independent validation shows that PGML SM has strong agreement with in-situ measurements (R = 0.868 and unbiased RMSE = 0.054 m3/m3). This study highlights the potential of PGML to enhance the accuracy of satellite SM, thereby supporting improved water resources and ecosystem management.

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Data availability

The global soil moisture dataset72 published in this study is available from Zenodo at https://doi.org/10.5281/zenodo.15826989. All external input datasets used in this research (e.g., SMAP brightness temperatures, ERA5-Land meteorological variables, MODIS NDVI) are publicly available from their original repositories, as cited in the manuscript.

Code availability

Data processing and analysis were conducted using Python version 3.13. The code is available on GitHub at https://github.com/SkyeFengg/PGML-SM.

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Acknowledgements

This work was supported by the Danish Data Science Academy, which is funded by the Novo Nordisk Foundation (NNF21SA0069429) and VILLUM FONDEN (40516). This work was also supported by the VILLUM Young Investigator 2024 project (00072051), the Novo Nordisk Starting Grant (NNF23OC0087612), the SCALE project (AgriFoodTure, Innovation Fund Denmark), NASA ECOSTRESS Science and Applications Program (80NSSC23K0308), NASA Early Career Investigator Program in Earth Science (80NSSC24K1057), and the Global Wetland Center (NNF23OC0081089, Novo Nordisk Foundation). Additional funding was provided by the Pioneer Center for Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2, and Aarhus University. The author acknowledge NASA, ESA, INRAE and ECMWF for providing global SM products and land surface features, and thank ISMN, AmeriFlux, JapanFlux, ICOS and previous studies for providing available in-situ SM measurements.

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Author notes

  1. These authors contributed equally: Sijia Feng, Aoyang Li.

Authors and Affiliations

  1. Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark

    Sijia Feng, Klaus Butterbach-Bahl, Claire Treat & Sheng Wang

  2. Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Aoyang Li, Rui Zhou, Kaiyu Guan & Sheng Wang

  3. Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Aoyang Li, Rui Zhou & Kaiyu Guan

  4. National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Aoyang Li, Rui Zhou & Kaiyu Guan

  5. Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Aoyang Li, Rui Zhou & Kaiyu Guan

  6. Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, Garmisch-Partenkirchen, 82467, Germany

    Klaus Butterbach-Bahl

  7. Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, 55108, USA

    Zhenong Jin

  8. Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, Copenhagen, 1165, Denmark

    Majken C. Looms

  9. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Sherrie Wang

  10. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Sherrie Wang

  11. Department of Computer Science, University of Copenhagen, Copenhagen, 2100, Denmark

    Christian Igel

  12. Pioneer Centre for Artificial Intelligence, Copenhagen, 1350, Denmark

    Christian Igel

  13. Department of Agroecology, Aarhus University, Blichers Allé 20, Tjele, 8830, Denmark

    Jørgen Eivind Olesen

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Contributions

S.F. and S.W. conceived this research and drafted the manuscript; S.F. and A.L. performed the experiments and data processing; S.W. provided supervision and guidance throughout the experiment and analysis; and all the authors reviewed and revised the manuscript.

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Correspondence to Sheng Wang.

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Feng, S., Li, A., Zhou, R. et al. Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning. Sci Data (2026). https://doi.org/10.1038/s41597-026-06721-6

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  • Received: 10 July 2025

  • Accepted: 26 January 2026

  • Published: 12 February 2026

  • DOI: https://doi.org/10.1038/s41597-026-06721-6

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