Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
Problem
Estimating 10-hour dead fuel moisture content across the contiguous US is needed to manage and mitigate wildland fires.
Why it matters
Fuel moisture monitoring is crucial for managing and mitigating wildland fire impacts.
What the AI did
Machine learning models were trained on National Water Model and HRRR variables, static surface properties, and VIIRS satellite reflectance and land surface temperature retrievals to estimate FMC.
Results
Skill gains of +44% versus daily climatology RMSE and +24% versus hourly climatology RMSE after hyperparameter optimization.
Impact
Peer-reviewed models with stated skill improvements over climatology baselines for CONUS-wide FMC estimation.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | John S. Schreck, William Petzke, Pedro A. Jiménez, Thomas Brummet, Jason C. Knievel, Eric P. James, Branko Kosović, David John Gagne |
| Citations | 14 (OpenAlex, at import time) |
| References | 38 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs15133372 |
| Open access | gold |
| Keywords | fuel moisture content, machine learning, VIIRS, wildfire, remote sensing, numerical weather prediction |
Importance rating
significant — thousands to millions affected
Abstract states FMC monitoring is crucial for managing and mitigating wildland fire impacts, with models covering the contiguous US, a large-scale fire-management application.
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