  Monitoring the fuel moisture content (FMC) of vegetation is crucial for
managing and mitigating the impact of wildland fires. The combination of in
situ FMC observations with numerical weather prediction (NWP) models and
satellite retrievals has enabled the development of machine learning (ML)
models to estimate dead FMC retrievals over the contiguous US (CONUS). In this
study, ML models were trained using variables from the National Water Model and
the High-Resolution Rapid Refresh (HRRR) NWP models, and static variables
characterizing the surface properties, as well as surface reflectances and land
surface temperature (LST) retrievals from the VIIRS instrument on board the
Suomi-NPP satellite system. Extensive hyper-parameter optimization yielded
skillful FMC models compared to a daily climatography RMSE (+44\%) and to an
hourly climatography RMSE (+24\%). Furthermore, VIIRS retrievals were important
predictors for estimating FMC, contributing significantly as a group due to
their high band-correlation. In contrast, individual predictors in the HRRR
group had relatively high importance according to the explainability techniques
used. When both HRRR and VIIRS retrievals were not used as model inputs, the
performance dropped significantly. If VIIRS retrievals were not used, the RMSE
performance was worse. This highlights the importance of VIIRS retrievals in
modeling FMC, which yielded better models compared to MODIS. Overall, the
importance of the VIIRS group of predictors corroborates the dynamic
relationship between the 10-h fuel and the atmosphere and soil moisture. These
findings emphasize the significance of selecting appropriate data sources for
predicting FMC with ML models, with VIIRS retrievals and selected HRRR
variables being critical components in producing skillful FMC estimates.
