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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 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 machine learningderived estimates with existing approaches can indicate the potential uncertainties in detection. The approach has been applied to detect gas flaring activities over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that a large fraction of the light-emission detections from probable weaker flares are missed by the VIIRS thermal bands and existing datasets. Our method does not require any a priori knowledge of combustion signature and is extendible to events, such as biomass and waste burning, and can be scaled globally using daily Black Marble observations for transparent emission estimate reporting. This is expected to have a twofold impact on satellite-derived emission estimates, firstly, through the inclusion of the light-emission signal enabling the detection of probable weaker combustion, and secondly, by generating potential uncertainty estimates of the detections, thereby increasing transparency of the derived datasets.
Topic : Theme 2: State of play in integrated approaches for advanced GHG emission estimates and the way forward to operational services.
Reference : T2-B23
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