Dead fuel moisture content (FMC) is the water content in dead vegetation expressed as a percentage of dry weight, and is a key determinant of fire ignition, spread rate and intensity. Regarding fuel, size classes are defined by their nominal response time to atmospheric conditions: 1-hour fuels (<6 mm diameter, such as litter and fine twigs) equilibrate ⅔ of their moisture within 1 hour, while 10-hour fuels (6–25 mm diameter, including small branches and woody debris) require 10 hours. Direct measurement of fuel moisture involves field sampling, where collected material is weighed, at 105°C for at least 8 hours, and reweighed to calculate FMC as ratio of water mass to dry mass (expressed as a percentage). This drying temperature is recommended over lower values (<105°C), which have been shown to underestimate FMC (Matthews 2010).

FMC can be estimated from readily available meteorological variables using: (1) empirical models based on temperature (T) and relative humidity (RH), such as the fuel moisture index FMI (Sharples et al., 2009) as the predictor variable; (2) empirical models based on vapor pressure deficit (VPD) that describe the inverse exponential relationship between VPD and FMC (Rodrigues et al., 2024; Resco de Dios et al., 2015); or (3) complex process-based models simulating heat and moisture fluxes (Matthews, 2006).

Recent research demonstrates that simple models based on VPD or weighted differences between temperature and humidity can perform as well as sophisticated approaches, making them highly suitable for operational field use (Sharples et al., 2024). Building on this approach, the proposed method is based on the calculation of FMI from weighted differences between air temperature (°C) and relative humidity (%) (see (1) in the Calculation section). FMC is then derived by applying a scaling coefficient α determined through local model calibration considering different fuels (Sharples et al., 2011). The approach leverages direct meteorological measurements from field sensors (temperature and relative humidity stations), readily available online weather data from meteorological networks, or operational weather forecasts. This combination of model simplicity and data accessibility enables rapid fuel moisture assessment in operational settings with minimal computational requirements, requiring only basic measurements and simple calculations that can be performed with hand-held weather meters and basic calculators.