Zhan et al. (2026) Live fuel moisture mapping in unburned chaparral areas of Southern California with MODIS and VIIRS

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-07-02
• Authors: Yanni Zhan, Seth H. Peterson, Dar A. Roberts, Charles Jones, Alan T. Murray
• DOI: 10.1016/j.rse.2026.115546

Research Groups

• Department of Geography, University of California, Santa Barbara
• Climate Hazards Center, University of California, Santa Barbara
• Earth Research Institute, University of California, Santa Barbara

Short Summary

This study developed a framework to estimate live fuel moisture (LFM) in unburned chaparral areas of Southern California using MODIS and VIIRS satellite data. The framework was evaluated for its ability to provide spatially transferable and temporally consistent LFM monitoring for fire risk assessment.

Objective

• Develop an interpretable, phenology-informed satellite framework to support spatially transferable and temporally consistent LFM monitoring in chaparral ecosystems.

Study Configuration

• Spatial Scale: Southern California chaparral areas at a 500-m resolution.
• Temporal Scale: Daily surface reflectance observations from MODIS (2003-2022) and VIIRS (2011-2022).

Methodology and Data

• Models used:
  • Multiple linear regression (MLR)
  • Random forest (RF)
• Data sources:
  • Globe-LFMC 2.0 dataset for LFM field measurements
  • MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data
  • VIIRS BRDF/NBAR/Albedo products

Main Results

• The study found that the random forest model achieved higher accuracy with the full dataset, but its performance declined substantially under spatial cross-validation.
• Multiple linear regression showed more stable performance between full-dataset evaluation and spatial cross-validation.
• MODIS models provided higher predictive skill overall than VIIRS, whereas the M-band-only VIIRS configuration produced the highest VIIRS performance.

Contributions

• This study shows that an interpretable, phenology-informed satellite framework can support spatially transferable and temporally consistent LFM monitoring for chaparral fire risk assessment.
• The study provides a near-real-time LFM estimation framework using an analog-year approach with survival analysis to estimate annual vegetation attributes and key phenological transitions.

Funding

• This research was funded by the National Oceanic and Atmospheric Administration (NOAA) under grant number [insert grant number].

Citation

@article{Zhan2026Live,
  author = {Zhan, Yanni and Peterson, Seth H. and Roberts, Dar A. and Jones, Charles and Murray, Alan T.},
  title = {Live fuel moisture mapping in unburned chaparral areas of Southern California with MODIS and VIIRS},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115546},
  url = {https://doi.org/10.1016/j.rse.2026.115546}
}