Rhadiouini et al. (2026) A hybrid Physics-Guided Machine Learning for Soil Moisture Monitoring and Drought Assessment from CYGNSS in Complex Terrain

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Charafa El Rhadiouini, Shuanggen Jin, Emmanuel Yeboah, Isaac Sarfo, Abraham Okrah
• DOI: 10.1109/tgrs.2026.3677267

Research Groups

Not available from the provided text.

Short Summary

This paper introduces a hybrid Physics-Guided Machine Learning (PGML) framework to enhance soil moisture monitoring and drought assessment capabilities using CYGNSS data, particularly focusing on applications in complex terrain.

Objective

• To develop and evaluate a hybrid Physics-Guided Machine Learning approach for soil moisture monitoring.
• To apply this approach for drought assessment using CYGNSS data.
• To address the specific challenges of soil moisture retrieval and drought assessment in complex terrain environments.

Study Configuration

• Spatial Scale: Regional to local scale, specifically in "complex terrain." No specific geographic area is provided.
• Temporal Scale: Implied continuous or periodic monitoring and assessment. No specific duration or frequency is provided.

Methodology and Data

• Models used: A "hybrid Physics-Guided Machine Learning" (PGML) framework. Specific underlying physics or machine learning models are not detailed in the provided text.
• Data sources: Cyclone Global Navigation Satellite System (CYGNSS) data.

Main Results

Not available from the provided text.

Contributions

• Development of a novel hybrid Physics-Guided Machine Learning framework for soil moisture applications.
• Demonstration of CYGNSS data utility for soil moisture monitoring and drought assessment in challenging complex terrain.

Funding

Not available from the provided text.

Citation

@article{Rhadiouini2026hybrid,
  author = {Rhadiouini, Charafa El and Jin, Shuanggen and Yeboah, Emmanuel and Sarfo, Isaac and Okrah, Abraham},
  title = {A hybrid Physics-Guided Machine Learning for Soil Moisture Monitoring and Drought Assessment from CYGNSS in Complex Terrain},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3677267},
  url = {https://doi.org/10.1109/tgrs.2026.3677267}
}