Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments

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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 retri...