Probability of Detection and Multi-Sensor Persistence of Methane Emissions from Coincident Airborne and Satellite Observations

Satellites are becoming a widely used measurement tool for methane detection and quantification. The landscape of satellite instruments with some methane point-source quantification capabilities is growing. Combining information across available sensor platforms could be pivotal for understanding trends and uncertainties in source-level emissions. However, to effectively combine information across sensors of varying performance levels, the probability of detection (POD) for all instruments must be well characterized, which is time-consuming and costly, especially for satellites. In August of 2023, we timed methane-sensing aerial surveys from the Global Airborne Observatory (GAO) to overlap with observations from the NASA Earth Surface Mineral Dust Source Investigation (EMIT). We show how these co-incident observations can be used to determine and verify the detection limits of EMIT and to develop and test a multi-sensor persistence framework. Under favorable conditions the 90% probability of detection at 3 m/s for EMIT is 1060 kg/hr. We further derive a Bayesian model to infer probabilistically whether non-detected emissions were truly off, and we use this model to assess the intermittency of emissions across GAO and EMIT. Time-averaged emission rates from persistent sources can be underestimated if POD is not characterized and if differences in POD across multi-sensor frameworks are not properly accounted for.