Untethered from earthly constraints: A spatial seven-day ahead machine-learning forest fuel moisture forecasting system, independent of real-time sensor networks

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.