Obregón et al. (2026) Assessment of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America

Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-10-06
• Authors: Fabricio Matías Obregón, Lucini Magdalena, Omar Vicente Müller, Romina Carla Ruscica, Manuel Pulido
• DOI: 10.1175/jhm-d-26-0062.1

Research Groups

• Facultad de Ciencias Exactas y Naturales y Agrimensura, Universidad Nacional del Nordeste (Argentina)
• Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) (Argentina)
• Instituto Franco-Argentino de Estudios sobre el Clima y sus Impactos – IRL 3351 – CNRS-CONICET-IRD-UBA (Argentina)

Short Summary

This study evaluates the performance of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America, focusing on the endorheic Pampas region.

Objective

• Evaluate the effectiveness of off-line observation bias-correction methods within a land data assimilation framework for estimating surface soil moisture (SSM) during the 2022 dry season in central-northeastern Argentina.

Study Configuration

• Spatial Scale: Regional scale, focusing on the endorheic Pampas region in Southeastern South America.
• Temporal Scale: Dry season of 2022.

Methodology and Data

• Models used: Noah-MP land surface model and an ensemble Kalman filter.
• Data sources: Satellite soil moisture data from SMAP and SMOS missions, as well as independent in-situ soil measurements.

Main Results

• The proposed bias correction method, which combines climatological statistics computed using homogeneous soil texture pixels with a 45-day moving temporal window to smooth cumulative distribution function (CDF) evolution, shows improved statistical sampling in CDF matching.
• This method produces measurable impact on SSM estimates, particularly during dry periods, and exhibits better agreement with independent in-situ soil measurements than standard CDF matching.

Contributions

• The study contributes to the development of more accurate methods for bias correction in satellite soil moisture data assimilation, which is essential for hydrological monitoring and agricultural management in drought-prone regions.

Funding

• This research was funded by CONICET (Argentina) and IRL 3351 – CNRS-CONICET-IRD-UBA (Argentina).

Citation

@article{Obregón2026Assessment,
  author = {Obregón, Fabricio Matías and Magdalena, Lucini and Müller, Omar Vicente and Ruscica, Romina Carla and Pulido, Manuel},
  title = {Assessment of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-26-0062.1},
  url = {https://doi.org/10.1175/jhm-d-26-0062.1}
}