Cho et al. (2025) Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geophysical Research Letters
• Year: 2025
• Date: 2025-10-13
• Authors: Kyeungwoo Cho, Saji Abraham, Sujay V. Kumar, Jingfeng Wang
• DOI: 10.1029/2025gl117025

Research Groups

Not specified in abstract.

Short Summary

This study evaluates the relationship between Live Fuel Moisture (LFM) and remotely sensed Vegetation Water Content (VWC) and Soil Moisture (SM) derived from SMAP L-band brightness temperature, demonstrating that MEP-retrieved VWC serves as a strong, scalable proxy for LFM in the Western U.S. for wildfire risk assessment.

Objective

• To evaluate the relationship between Live Fuel Moisture (LFM) and Vegetation Water Content (VWC) and Soil Moisture (SM) retrieved from SMAP L-band brightness temperature using the Maximum Entropy Production (MEP) approach.

Study Configuration

• Spatial Scale: Western U.S.
• Temporal Scale: Not specified in abstract.

Methodology and Data

• Models used: Maximum Entropy Production (MEP) approach
• Data sources: SMAP L-band brightness temperature (remote sensing), in situ measurements of LFM, high-resolution vegetation coverage data.

Main Results

• MEP-retrieved Vegetation Water Content (VWC) exhibited a strong correlation (r > 0.6) with in situ measurements of Live Fuel Moisture (LFM) in the Western U.S.
• The integration of high-resolution vegetation coverage data enhanced the detection of sub-grid vegetation heterogeneity.
• Remote sensing-derived VWC demonstrates operational potential as a scalable proxy for LFM.

Contributions

• Demonstrates the operational potential of remote sensing-derived Vegetation Water Content (VWC) as a scalable proxy for Live Fuel Moisture (LFM).
• Supports the application of remotely sensed VWC in regional assessment of wildfire risk, addressing limitations of traditional labor-intensive field sampling.
• Highlights the benefit of integrating high-resolution vegetation coverage data for detecting sub-grid heterogeneity in LFM estimation.

Funding

Not specified in abstract.

Citation

@article{Cho2025Remote,
  author = {Cho, Kyeungwoo and Abraham, Saji and Kumar, Sujay V. and Wang, Jingfeng},
  title = {Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations},
  journal = {Geophysical Research Letters},
  year = {2025},
  doi = {10.1029/2025gl117025},
  url = {https://doi.org/10.1029/2025gl117025}
}