Moon et al. (2026) Depth of Liquid Water Infiltration in Greenland Firn Based on L-Band Radiometry, a Snow Physics Model, and Machine Learning

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Taylor D. Moon, J. T. Harper, Alamgir Hossan, Andreas Colliander
• DOI: 10.1109/tgrs.2026.3669024

Research Groups

[Information not available in the provided text.]

Short Summary

This study aims to quantify the depth of liquid water infiltration within Greenland's firn layer, leveraging a combination of L-band radiometry, a snow physics model, and machine learning methodologies.

Objective

• To quantify the depth of liquid water infiltration in Greenland firn using L-band radiometry, a snow physics model, and machine learning.

Study Configuration

• Spatial Scale: Greenland Firn
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: A snow physics model; Machine learning algorithms.
• Data sources: L-band radiometry.

Main Results

[Information not available in the provided text.]

Contributions

[Information not available in the provided text.]

Funding

[Information not available in the provided text.]

Citation

@article{Moon2026Depth,
  author = {Moon, Taylor D. and Harper, J. T. and Hossan, Alamgir and Colliander, Andreas},
  title = {Depth of Liquid Water Infiltration in Greenland Firn Based on L-Band Radiometry, a Snow Physics Model, and Machine Learning},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3669024},
  url = {https://doi.org/10.1109/tgrs.2026.3669024}
}