Wildfires consumed more than 3.5 million hectares in the United States in 2018, and federal fire suppression costs topped US$3 billion. These fires destroyed more than 18,000 residences and caused the deaths of at least 85 people. Wildfire damages like these are not unique to the United States; they are a threat in many nations. Researchers from across the globe and across multiple scientific disciplines are working to improve fire danger rating systems to help protect natural resources and human health and safety. One new concept emerging as a valuable contribution to this effort is the integration of soil moisture information as a predictor of wildfire probability. Soil moisture, particularly within the zone where plant roots reside, is a key link between weather conditions, such as precipitation and temperature, and the characteristics of the live vegetative “fuel bed,” which include fuel moisture and fuel loads (weight of fuel per unit area). These dynamic vegetation characteristics, which strongly influence wildfire probability, can be challenging to model and monitor at relevant spatial and temporal scales using field data. Optical remote sensing of fuel moisture also presents challenges; for example, remote sensing models that predict live-fuel moisture contain relatively large margins of error that vary by vegetation type.Soil moisture monitoring capabilities, in contrast, have been steadily growing because of the development of in situ networks and dedicated satellites. Researchers have intuitively understood the relationships between soil moisture, fuel conditions, and wildfire occurrence for a long time. However, the increasing availability of soil moisture information is creating significant opportunities to quantify these relationships and incorporate them into new or existing weather-based fuel moisture models and fire danger rating systems.