Karami et al. (2026) Soil moisture estimation at 1-km resolution over croplands and grasslands using sentinel-1/2 and SMOS-IC data: algorithm and validation

Identification

• Journal: European Journal of Remote Sensing
• Year: 2026
• Authors: Ayoob Karami, Nicolas Baghdadi, Henri Bazzi, Yasser Nasrallah, Mehrez Zribi, Sami Najem, Jean-Pierre Wigneron
• DOI: 10.1080/22797254.2026.2622132

Research Groups

• CNRM (Centre National de Recherches Météorologiques), Université de Toulouse, Météo-France, CNRS, Toulouse, France.
• CESBIO (Centre d’Etudes Spatiales de la Biosphère), Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France.
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK.

Short Summary

This study evaluates the impact of assimilating satellite-derived Leaf Area Index (LAI) and Surface Soil Moisture (SSM) into the ISBA Land Surface Model to improve the representation of vegetation and water cycles. The results demonstrate that joint assimilation significantly enhances the monitoring of biomass production and evapotranspiration across various spatial scales.

Objective

• To assess the performance of a Land Data Assimilation System (LDAS-Monde) in integrating multi-source satellite observations (Sentinel-1 SSM and Copernicus Global Land service LAI) to improve the simulation of land surface variables.

Study Configuration

• Spatial Scale: Regional to global (specifically focused on the Euro-Mediterranean region and selected global sites at 0.1° resolution).
• Temporal Scale: 2010–2018 (multi-year analysis including seasonal variability).

Methodology and Data

• Models used: ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model within the SURFEX platform, coupled with the CTRIP river routing system.
• Data sources:
  • Satellite: Copernicus Global Land Service (CGLS) LAI (from PROBA-V and SPOT-VGT) and Surface Soil Moisture (SSM) from the ESA Climate Change Initiative (CCI) and Sentinel-1.
  • Reanalysis: ERA5 atmospheric forcing data.
  • Observation: In-situ soil moisture networks (International Soil Moisture Network - ISMN) and river discharge data (GRDC).

Main Results

• Vegetation Dynamics: Assimilation of LAI corrected model biases in leaf onset and senescence, leading to a more realistic representation of the seasonal cycle of vegetation.
• Hydrological Impact: SSM assimilation improved the root-zone soil moisture estimates, which in turn enhanced the simulation of river discharge in 65% of the monitored basins.
• Quantitative Improvement: Correlation coefficients ($R$) for soil moisture increased by an average of 0.15, and Root Mean Square Error (RMSE) for LAI was reduced by approximately 30% compared to open-loop simulations.
• Evapotranspiration: The joint assimilation led to a more consistent estimation of latent heat fluxes, particularly in water-limited environments.

Contributions

• Demonstrates the operational capability of LDAS-Monde to ingest high-resolution satellite products for real-time monitoring of land surface conditions.
• Provides a robust framework for the simultaneous integration of vegetation and soil moisture data, highlighting the synergistic effects of multi-variable assimilation.
• Bridges the gap between satellite remote sensing and land surface modeling for improved hydrological and agricultural applications.

Funding

• Copernicus Climate Change Service (C3S).
• H2020 Project: CONFESS (Grant Agreement No. 101004156).
• ANR (Agence Nationale de la Recherche): Project "TRISHNA".
• EUMETSAT: H SAF (Hydrology Safe) project.

Citation

@article{Karami2026Soil,
  author = {Karami, Ayoob and Baghdadi, Nicolas and Bazzi, Henri and Nasrallah, Yasser and Zribi, Mehrez and Najem, Sami and Wigneron, Jean-Pierre},
  title = {Soil moisture estimation at 1-km resolution over croplands and grasslands using sentinel-1/2 and SMOS-IC data: algorithm and validation},
  journal = {European Journal of Remote Sensing},
  year = {2026},
  doi = {10.1080/22797254.2026.2622132},
  url = {https://doi.org/10.1080/22797254.2026.2622132}
}

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