Li et al. (2025) Improvement of Snow Albedo Simulation Considering Water Content

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-30
• Authors: Fengyu Li, Kun Wu
• DOI: 10.3390/rs17233899

Research Groups

Not specified in the provided text.

Short Summary

This study developed a snow albedo model explicitly considering water content by integrating Maxwell–Garnett, Mie scattering, and four-stream discrete ordinates methods, demonstrating that liquid water content significantly impacts near-infrared albedo and validating its accuracy across diverse Chinese regions.

Objective

• To construct a snow albedo model with explicit consideration of water content and systematically analyze the influence of snow water content on snow albedo simulation.

Study Configuration

• Spatial Scale: Regional (Tibetan Plateau, Xinjiang, and Northeast China).
• Temporal Scale: Not explicitly specified, but implied to cover periods relevant for snowmelt and albedo changes.

Methodology and Data

• Models used: A newly constructed snow albedo model combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method.
• Data sources: Actual measurement cases (observations) from stations on the Tibetan Plateau, Xinjiang, and Northeast China.

Main Results

• Liquid water content is the key factor causing significant changes in albedo in the near-infrared band.
• The albedo of snow with small particle sizes exhibits higher sensitivity to water content.
• Water content in the surface layer of snow has a more pronounced effect on reducing albedo.
• The model provides a good simulation of albedo accuracy, with a bias of −0.0069 and a Root Mean Square Error (RMSE) of 0.0583 compared to observations.

Contributions

• Development of a novel snow albedo model that explicitly incorporates snow water content, enhancing physical mechanism expression.
• Systematic analysis highlighting the critical role of liquid water content, snow particle size, and surface layer water content on albedo.
• Demonstrated stable performance and good regional applicability of the model in complex environments across diverse regions of China.

Funding

Not specified in the provided text.

Citation

@article{Li2025Improvement,
  author = {Li, Fengyu and Wu, Kun},
  title = {Improvement of Snow Albedo Simulation Considering Water Content},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17233899},
  url = {https://doi.org/10.3390/rs17233899}
}