Dead fuel moisture content (DFMC) critically influences wildfire behaviour, and its modelling underpins many fire management decision support systems. Recent modelling advances have enabled accurate forecast of point-scale fuel moisture, but their reliance on continuous real-time sensor functionality creates operational vulnerabilities when sensors may fail. Maintaining sensor networks across large, remote domains is costly and unreliable. Therefore, we developed a spatially continuous DFMC forecast system that eliminates real-time sensor dependency by replacing sensor initialisation with remotely sensed and modelled proxies for landscape fuel moisture states. Using 23,354 site-day observations from 27 forested sites in Victoria, Australia, our machine learning model produces 7-day ahead sub-canopy DFMC forecasts with median RMSE of 11.5% and 12.8% for day 1 and 7. The approach delivers reliable spatial forecasts across forested landscapes without sensor-dependent vulnerabilities, representing a significant advancement in operational fire risk management by providing comprehensive coverage for wildfire suppression planning and prescribed burning. • We present the first ML model for sensor-independent spatial forest DFMC forecasts. • Training used 23,354 site-days from 27 below-canopy forest monitoring sites. • Our approach eliminates sensor failure vulnerabilities during wildfire events. • Day 1-7 forecasts achieve 11.5-12.8% RMSE across 150,000km 2 of forested landscape.
Keeble et al. (Sun,) studied this question.
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