Machine learning and VIIRS satellite retrievals for skillful Fuel Moisture Content monitoring in wildfire management

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

To Access Resource:

Questions? Email Resource Support Contact:

  • opensky@ucar.edu
    UCAR/NCAR - Library

Resource Type publication
Temporal Range Begin N/A
Temporal Range End N/A
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links

Related Dataset #1 : Datasets used in "Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management"

Related Preprint #1 : Why do tree-based models still outperform deep learning on tabular data?

Additional Information N/A
Resource Format PDF
Standardized Resource Format PDF
Asset Size N/A
Legal Constraints

Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email opensky@ucar.edu
Resource Support Organization UCAR/NCAR - Library
Distributor N/A
Metadata Contact Name N/A
Metadata Contact Email opensky@ucar.edu
Metadata Contact Organization UCAR/NCAR - Library

Author Schreck, John S.
Petzke, William
Jiménez Munoz, Pedro A.
Brummet, Thomas
Knievel, Jason C.
James, E.
Kosović, Branko
Gagne, David John
Publisher UCAR/NCAR - Library
Publication Date 2023-07-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
Progress N/A
Metadata Date 2025-07-11T15:16:32.820888
Metadata Record Identifier edu.ucar.opensky::articles:26501
Metadata Language eng; USA
Suggested Citation Schreck, John S., Petzke, William, Jiménez Munoz, Pedro A., Brummet, Thomas, Knievel, Jason C., James, E., Kosović, Branko, Gagne, David John. (2023). Machine learning and VIIRS satellite retrievals for skillful Fuel Moisture Content monitoring in wildfire management. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7wd44k4. Accessed 03 August 2025.

Harvest Source