Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments
Description
Satellite-based fire detection provides critical data for fire management, fire spread modeling, air quality forecasts, and assessments of fire impacts on ecosystems and communities. Current fire detection algorithms, whether physics-based or machine learning (ML)-based, frequently fail when wildfires are obscured by dense clouds or smoke, creating data gaps that degrade the quality of air quality and fire emissions estimates. This project will develop an explainable multitask ML model for fire detection that is integrated with cloud and aerosol retrieval to enhance fire detection capabilities under clouds.
Detailed example
Outputs will be joint retrievals of cloud and aerosol vertical profile information and fire detections.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The research has the potential to subtantially improve satellite-based fire detection, a key application and societal benefit from NASA's Earth science program, based on more consistent detection of fire activity needed for fire tracking and situational awareness, and improved detection under difficult observing conditions, including on days when extreme fire behavior generates deep injection of smoke (pyrocumulonimbus or PyroCb).
Controls / human review
ATO: Not reported; PIA: Not published