A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

One of the primary variables affecting ignition and spread of wildfire is fuel moisture content (FMC). Live FMC (LFMC) is responsive to long term climate and plant adaptations to drought, requiring remote sensing for monitoring of spatial and temporal variations in LFMC. Liquid water has strong absorption features in the near- and shortwave-infrared spectral regions, which provide a physical basis for direct estimation of LFMC. Complexity introduced by biophysical and biochemical properties at leaf and canopy scales presents theoretical and methodological problems that must be addressed before remote sensing can be used for operational monitoring of LFMC. The objective of this paper is to review the use of remotely sensed data for estimating LFMC, with particular concern towards the operational use of LFMC products for fire risk assessment. Relationships between LFMC and fire behavior have been found in fuel ignition experiments and at landscape scales, but the complexity of fire interactions with fuel structure has prevented linking LFMC to fire behavior at intermediate scales. Changes in LFMC have both direct (liquid water absorption) and indirect (pigment and structural changes) impacts on spectral reflectance. The literature is dominated by studies that have used statistical (empirical) and physical model-based methods applied to coarse resolution data covering the visible, near infrared, and/or shortwave infrared regions of the spectrum. Empirical relationships often have the drawback of being site-specific, while the selection and parameterization of physically-based algorithms are far more complex. Challenges remain in quantifying error of remote sensing-based LFMC estimations and linking LFMC to fire behavior and risk. The review concludes with a list of priority areas where advancement is needed to transition remote sensing of LFMC to operational use. •We review satellite LFMC products and their operational use for fire assessment.•The literature is dominated by statistical and physical model-based methods.•Statistical methods are site-specific.•The parameterization of physical models is complex.•Challenges: quantifying estimation errors and linking LFMC to fire behavior/risk.