Li et al. (2026) Accelerating Numerical Electromagnetic Scattering Models for Snow Microwave Emission Using Machine Learning Surrogates

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: X.R. Li, Chuan Xiong
• DOI: 10.1109/tgrs.2026.3658678

Research Groups

Information not available in the provided text.

Short Summary

This paper focuses on accelerating numerical electromagnetic scattering models, which are used to simulate snow microwave emission, by employing machine learning surrogates. The core objective is to improve the computational efficiency of these models.

Objective

• To accelerate numerical electromagnetic scattering models for snow microwave emission using machine learning surrogates.

Study Configuration

• Spatial Scale: Information not available in the provided text.
• Temporal Scale: Information not available in the provided text.

Methodology and Data

• Models used: Numerical electromagnetic scattering models, Machine Learning Surrogates.
• Data sources: Information not available in the provided text.

Main Results

Information not available in the provided text, beyond the implied successful acceleration of models.

Contributions

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Funding

Information not available in the provided text.

Citation

@article{Li2026Accelerating,
  author = {Li, X.R. and Xiong, Chuan},
  title = {Accelerating Numerical Electromagnetic Scattering Models for Snow Microwave Emission Using Machine Learning Surrogates},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3658678},
  url = {https://doi.org/10.1109/tgrs.2026.3658678}
}