Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets

TL;DR

This paper presents a self-supervised machine learning method for identifying and tracking smoke plumes and active fires in satellite and sub-orbital remote sensing data, enhancing wildfire monitoring and climate impact analysis.

Contribution

It introduces a novel self-supervised ML approach that fuses multi-instrument remote sensing data for accurate fire and smoke detection.

Findings

Successfully differentiates fire pixels and smoke plumes from background imagery.

Generates per-instrument and fused smoke and fire masks.

Potential to improve wildfire monitoring and climate impact studies.

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. The demonstrated approach successfully differentiates fire pixels and smoke plumes from background imagery, enabling the generation of a per-instrument smoke and fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has a potential to enhance operational wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification12 and tracking and could improve climate impact studies through fusion data from independent instruments.