This analysis demonstrates a strong correlation between live fuel moisture and vegetation water content, suggesting remote sensing is effective for wildfire assessment.
Live Fuel Moisture (LFM) is a critical parameter for wildfire risk assessment, traditionally measured by labor‐intensive field sampling. However, sampled LFM data are influenced by site‐specific factors, such as local vegetation types and plant traits, and are often collected retrospectively after wildfire events, making it difficult to obtain pre‐fire data for predictive applications. Here, we evaluate the relationship between LFM and Vegetation Water Content (VWC) and Soil Moisture (SM) retrieved from SMAP L‐band brightness temperature using the Maximum Entropy Production (MEP) approach. The MEP‐retrieved VWC exhibited strong correlation with in situ measurements of LFM ( r > 0.6) in the Western U.S. The integration of high‐resolution vegetation coverage data enhances the detection of sub‐grid vegetation heterogeneity. This study demonstrates the operational potential of remote sensing derived VWC as a scalable proxy of LFM, supporting its application in regional assessment of wildfire risk.
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Cho et al. (2025) studied this question.
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