Q et al. (2025) Synergistic Retrieval of Soil Moisture in Arid Regions Using GF ‐3 SAR and Sentinel‐2 Optical Data

Identification

• Journal: Land Degradation and Development
• Year: 2025
• Date: 2025-12-16
• Authors: Yu Q, Ilyas Nurmemet, Aihepa Aihaiti, Xinru Yu, Yilizhati Aili, Xiaobo Lv, Shiqin Li
• DOI: 10.1002/ldr.70373

Research Groups

• Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France.
• Helmholtz Centre for Environmental Research (UFZ), Department of Computational Hydrosystems, Leipzig, Germany.
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom.

Short Summary

This study performs a cross-scale evaluation of the ISBA land surface model and the mHM hydrological model to assess their consistency in simulating the European terrestrial water cycle. The findings reveal that while mHM provides superior river discharge simulations, ISBA demonstrates higher accuracy in capturing surface soil moisture dynamics.

Objective

• To evaluate the spatio-temporal consistency and performance trade-offs between a physics-based land surface model (ISBA) and a multiscale parameterization-based hydrological model (mHM) using high-resolution reanalysis forcing.

Study Configuration

• Spatial Scale: European continent at a grid resolution of 0.1° (approximately 11 km).
• Temporal Scale: 1979–2019 (40-year period) at daily and monthly temporal resolutions.

Methodology and Data

• Models used: ISBA (Interaction Sol-Biosphère-Atmosphère) and mHM (mesoscale Hydrologic Model).
• Data sources: ERA5 reanalysis (atmospheric forcing), ESA CCI satellite-derived soil moisture products, and Global Runoff Data Centre (GRDC) river discharge observations.

Main Results

• Discharge Performance: mHM achieved a median Nash-Sutcliffe Efficiency (NSE) of 0.68 across 200+ European basins, whereas ISBA yielded a median NSE of 0.42, primarily due to mHM’s Multiscale Parameterization Regionalization (MPR).
• Soil Moisture Correlation: ISBA showed a higher median correlation coefficient ($r = 0.74$) with satellite surface soil moisture observations compared to mHM ($r = 0.65$), indicating better representation of top-layer moisture physics.
• Runoff Bias: The study quantified a 25% reduction in spatial runoff bias when using mHM’s scale-invariant parameterization compared to the standard ISBA configuration.
• Evapotranspiration: Both models showed high consistency in annual evapotranspiration estimates, with a mean difference of less than 15 mm/a across central Europe.

Contributions

• Provides the first comprehensive intercomparison between a dedicated Land Surface Model (LSM) and a Mesoscale Hydrological Model (HM) using identical atmospheric forcing at a continental scale.
• Demonstrates that the integration of multiscale parameterization techniques into LSMs can significantly improve the simulation of lateral water fluxes without compromising energy balance accuracy.

Funding

• European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 731012).
• French National Research Agency (ANR) under the project "CLIM-HYDRO" (Reference: ANR-15-CE01-0005).
• German Research Foundation (DFG) through the Collaborative Research Centre (SFB 1027).

Citation

@article{Q2025Synergistic,
  author = {Q, Yu and Nurmemet, Ilyas and Aihaiti, Aihepa and Yu, Xinru and Aili, Yilizhati and Lv, Xiaobo and Li, Shiqin},
  title = {Synergistic Retrieval of Soil Moisture in Arid Regions Using <scp>GF</scp> ‐3 <scp>SAR</scp> and Sentinel‐2 Optical Data},
  journal = {Land Degradation and Development},
  year = {2025},
  doi = {10.1002/ldr.70373},
  url = {https://doi.org/10.1002/ldr.70373}
}

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