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Monitoring the fuel moisture content (FMC) of vegetation is crucial formanaging and mitigating the impact of wildland fires. The combination of insitu FMC observations with numerical weather prediction (NWP) models andsatellite retrievals has enabled the development of machine learning (ML)models to estimate dead FMC retrievals over the contiguous US (CONUS). In thisstudy, ML models were trained using variables from the National Water Model andthe High-Resolution Rapid Refresh (HRRR) NWP models, and static variablescharacterizing the surface properties, as well as surface reflectances and landsurface temperature (LST) retrievals from the VIIRS instrument on board theSuomi-NPP satellite system. Extensive hyper-parameter optimization yieldedskillful FMC models compared to a daily climatography RMSE (+44\%) and to anhourly climatography RMSE (+24\%). Furthermore, VIIRS retrievals were importantpredictors for estimating FMC, contributing significantly as a group due totheir high band-correlation. In contrast, individual predictors in the HRRRgroup had relatively high importance according to the explainability techniquesused. When both HRRR and VIIRS retrievals were not used as model inputs, theperformance dropped significantly. If VIIRS retrievals were not used, the RMSEperformance was worse. This highlights the importance of VIIRS retrievals inmodeling FMC, which yielded better models compared to MODIS. Overall, theimportance of the VIIRS group of predictors corroborates the dynamicrelationship between the 10-h fuel and the atmosphere and soil moisture. Thesefindings emphasize the significance of selecting appropriate data sources forpredicting FMC with ML models, with VIIRS retrievals and selected HRRRvariables being critical components in producing skillful FMC estimates.

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management