Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning.

Burn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders. Plain Language Summary: Post‐fire impact assessment and hazard mitigation heavily relies on burn severity metrics. Vegetation burn severity (VBS)—most relevant for ecological impacts—can be remotely sensed, but soil burn severity (SBS)—most relevant for hydrological impacts—requires laborious field recalibration of VBS maps. Lack of near real‐time SBS information is currently a data gap. Climate change‐driven weather whiplash can narrow the time interval between large wildfires and the ensuing precipitation events, requiring tools for rapid and accurate assessment of SBS without the need for laborious field recalibration of satellite‐derived metrics. Here we developed a physic‐informed Machine Learning model that can accurately develop SBS maps using readily available data without the need for resource and time intensive field assessments of burn severity. Additionally, SBS maps are developed for only a small fraction (less than 0.1%) of all wildfires in the US. Our model can develop SBS maps for all satellite‐observed wildfires in the western US, and has the potential to accomplish this task globally with further training. Finally, our model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. Key Points: Soil burn severity (SBS) assessment is key for hydrological hazard mitigationVegetation burn severity (VBS) can be remotely sensed, SBS is measured in the fieldPhysics‐informed Machine Learning models to translate VBS to SBS are developed [ABSTRACT FROM AUTHOR]