Potentially Underestimated Gas Flaring Activities -A New Approach to
Detect Combustion Using Machine Learning and NASA's Black Marble Product
Suite
Abstract
Monitoring changes in greenhouse gas (GHG) emission is critical for
assessing climate mitigation efforts towards the Paris Agreement goal. A
crucial aspect of science-based GHG monitoring is to provide objective
information for quality assurance and uncertainty assessment of the
reported emissions. Emission estimates from combustion events (gas
flaring and biomass burning) are often calculated based on activity data
(AD) from satellite observations, such as those detected from the
Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-NPP
and NOAA-20 satellites. These estimates are often incorporated into
carbon models for calculating emissions and removals. Consequently,
errors and uncertainties associated with AD propagate into these models
and impact emission estimates. Deriving uncertainty of AD is therefore
crucial for transparency of emission estimates but remains a challenge
due to the lack of evaluation data or alternate estimates. This work
proposes a new approach using machine learning (ML) for combustion
detection from NASA’s Black Marble product suite and explores the
assessment of potential uncertainties through comparison with existing
datasets. We jointly characterize combustion using thermal and light
emission signals, with the latter improving detection of probable weaker
combustion with less distinct thermal signatures. Being methodologically
independent, the differences in ML-derived estimates with existing
approaches can indicate the potential uncertainties in detection. The
approach was applied to detect gas flaring activities over the Eagle
Ford Shale, Texas. We analyzed the spatio-temporal variations in
detections and found that approximately 79.04% and 72.14% of the light
emission-based detections are missed by ML-derived detections from VIIRS
thermal bands and existing datasets, respectively. The region was
impacted by the winter storm Uri which resulted in a significant
reduction of flaring activities followed by a post-storm resumption. Our
method is extendible to combustion events, such as biomass and waste
burning, and can be scaled globally for transparent emission estimate
reporting.