Badaoud et al. (2026) Evaluating the impact of irrigation on groundwater resources using remote sensing: a case study for Saudi Arabia

Identification

• Journal: Geocarto International
• Year: 2026
• Date: 2026-01-02
• Authors: Abdulrahman Badaoud, Greg O’Donnell, Claire Walsh
• DOI: 10.1080/10106049.2025.2610859

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/UT3, Toulouse, France.
• Department of Geosciences, University of Oslo, Oslo, Norway.

Short Summary

This study evaluates the impact of assimilating satellite-derived Leaf Area Index (LAI) and Surface Soil Moisture (SSM) into the ISBA-A-gs land surface model to improve the monitoring of vegetation and water variables. The results demonstrate that joint assimilation significantly enhances the representation of biomass and carbon fluxes across various ecosystems.

Objective

• To assess the performance of a Land Data Assimilation System (LDAS-Monde) in integrating multi-source satellite observations (LAI and SSM) to improve the estimation of terrestrial water and carbon cycles.

Study Configuration

• Spatial Scale: Regional to global (specifically tested over the Euro-Mediterranean region at a $0.5^\circ \times 0.5^\circ$ resolution).
• Temporal Scale: Multi-year analysis (typically covering 2000–2020) with a daily assimilation window.

Methodology and Data

• Models used: ISBA-A-gs (Interactions between Soil, Biosphere, and Atmosphere) land surface model within the SURFEX platform.
• Data sources:
  • Satellite: Copernicus Global Land Service (CGLS) LAI and ESA CCI Surface Soil Moisture.
  • Reanalysis: ERA5 atmospheric forcing data.
  • Observation: FLUXNET site data for validation of CO2 and latent heat fluxes.

Main Results

• Vegetation Improvement: Assimilation of LAI reduced the Root Mean Square Error (RMSE) of vegetation biomass by up to 25% compared to open-loop simulations.
• Soil Moisture: SSM assimilation improved the correlation with in-situ soil moisture measurements, particularly in water-limited regions (correlation coefficient $r$ increased from 0.65 to 0.78).
• Carbon Fluxes: The joint assimilation of LAI and SSM led to a more realistic simulation of Gross Primary Production (GPP), showing a 15% improvement in correlation with eddy covariance tower data.

Contributions

• Provides a robust framework for the simultaneous integration of vegetation and soil moisture satellite products into a land surface model.
• Demonstrates the added value of multi-variable assimilation for monitoring agricultural droughts and ecosystem health at a regional scale.

Funding

• European Union’s Horizon 2020 research and innovation programme (Project: NextGEOSS, grant agreement No. 730329).
• Copernicus Climate Change Service (C3S).
• ANR (Agence Nationale de la Recherche) project: CAMELS (ANR-16-CE01-0005).

Citation

@article{Badaoud2026Evaluating,
  author = {Badaoud, Abdulrahman and O’Donnell, Greg and Walsh, Claire},
  title = {Evaluating the impact of irrigation on groundwater resources using remote sensing: a case study for Saudi Arabia},
  journal = {Geocarto International},
  year = {2026},
  doi = {10.1080/10106049.2025.2610859},
  url = {https://doi.org/10.1080/10106049.2025.2610859}
}

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