Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the Fire Influence on Regional to Global Environments and Air Quality Datasets

Abstract

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% increase in agreement between our produced masks and high-certainty hand-labeled pixels, relative to evaluated operational products, the demonstrated approach successfully differentiates active fire pixels and smoke plumes from background imagery. This enables the generation of a per-instrument smoke and active fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has the potential to enhance operational active wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification and tracking and could improve climate impact studies through fusion data from independent instruments.