Helili et al. (2025) Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-12-30
• Authors: Pariha Helili, Xiaojun Li, Jean‐Pierre Wigneron, Gabrielle Jacinthe Maria de Lannoy, Jian Peng, Frédéric Frappart, J. Zeng, Yao Xiao, L. Karthikeyan, Patricia de Rosnay, Zanpin Xing, Ardeshir Ebtehaj, Andreas Colliander, Preethi Konkathi, Ke Zhang, Lei Fan
• DOI: 10.1016/j.rse.2025.115207

Research Groups

• Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China
• Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
• INRAE, UMR1391 ISPA, Universit´e de Bordeaux, Villenave d’Ornon, France
• Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
• Department of Remote Sensing, Helmholtz Centre for Environmental Research−UFZ, Leipzig, Germany
• Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany
• State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
• Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India
• Interdisciplinary program (IDP) in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
• Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, China
• Saint Anthony Falls Laboratory, Department of Civil Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN, USA
• Finnish Meteorological Institute, Helsinki, Uusimaa, Finland

Short Summary

This study systematically evaluated 23 global satellite and model-based soil moisture products using 68 Cosmic Ray Neutron Sensing (CRNS) observations across Europe, finding that SMAP-INRAE-BORDEAUX (SMAP-IB) retrievals showed the superior consistency with CRNS measurements.

Objective

• To systematically evaluate 23 gridded soil moisture products (including single-sensor satellite, multi-sensor merged, and model-based products) using Cosmic Ray Neutron Sensing (CRNS) observations across Europe to assess their performance, identify biases, and understand factors influencing their accuracy.

Study Configuration

• Spatial Scale: Europe (68 CRNS measurement sites); CRNS observation radius of 130–240 m; global gridded soil moisture products.
• Temporal Scale: Not explicitly defined for the evaluated data in the provided text.

Methodology and Data

• Models used: Various model-based soil moisture products; Satellite retrieval algorithms (e.g., SMAP-INRAE-BORDEAUX (SMAP-IB), AMSR2-LPRM, CCI/C3S).
• Data sources:
  • Reference data: Cosmic Ray Neutron Sensing (CRNS) observations from 68 sites across Europe.
  • Evaluated products: 23 global gridded soil moisture products, including single-sensor satellite, multi-sensor merged, and model-based products (e.g., SMAP-IB, CCI/C3S, AMSR2-LPRM).

Main Results

• The SMAP-INRAE-BORDEAUX (SMAP-IB) soil moisture retrievals demonstrated superior consistency with CRNS measurements, achieving a correlation coefficient (R) of 0.80 and an unbiased root mean square error (ubRMSE) of 0.050 m³/m³.
• CCI/C3S combined active-passive soil moisture products ranked second, with R > 0.75 and ubRMSE < 0.060 m³/m³.
• Bias analysis revealed that 17 products had a negative bias ranging from −0.003 m³/m³ to −0.190 m³/m³ against CRNS measurements.
• AMSR2-LPRM at C1 and C2 bands, and CCI/C3S active and passive products, exhibited a positive bias ranging from 0.011 m³/m³ to 0.161 m³/m³.
• The performance of all soil moisture products, in terms of R and ubRMSE, degraded with increasing vegetation density, topographic complexity, and soil wetness.
• Most products showed the lowest ubRMSE and highest R values in cropland compared to other land cover types.

Contributions

• Provides a comprehensive and systematic evaluation of 23 diverse global soil moisture products using field-scale CRNS observations, effectively mitigating the spatial representativeness mismatch inherent in traditional point observations.
• Identifies the best-performing satellite and model-based soil moisture products (e.g., SMAP-IB, CCI/C3S) and quantifies their accuracy and biases across a wide range of European conditions.
• Highlights the significant influence of environmental factors such as vegetation density, topographic complexity, soil wetness, and land cover on the accuracy of soil moisture products.
• Emphasizes the substantial potential of cosmic ray neutron sensing for robust validation of satellite and model-based soil moisture products, offering critical insights for algorithm refinement, product improvement, and hydrometeorological applications.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Helili2025Assessment,
  author = {Helili, Pariha and Li, Xiaojun and Wigneron, Jean‐Pierre and Lannoy, Gabrielle Jacinthe Maria de and Peng, Jian and Frappart, Frédéric and Zeng, J. and Xiao, Yao and Karthikeyan, L. and Rosnay, Patricia de and Xing, Zanpin and Ebtehaj, Ardeshir and Colliander, Andreas and Konkathi, Preethi and Zhang, Ke and Fan, Lei},
  title = {Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115207},
  url = {https://doi.org/10.1016/j.rse.2025.115207}
}