A drone-based approach for measurements of multiple greenhouse gases with minimized gas sensor drift for increased sensitivity and improved source area identification

Two important challenges with UAS-based measurements of greenhouse gases (GHGs) are large baseline drifts of gas sensors and source identification when there are several sources distributed within a footprint. Ambient conditions, such as temperature and humidity, known to influence the accuracy of gas sensors, change fast during flight at different altitudes and speeds. Big such drifts limit UAS-based measurements to high and often anthropogenic emissions as they cause strong gradients in concentration levels. Emissions across landscapes often generate much weaker concentration gradients and are also more extended, making fluxes and source areas more challenging to constrain.

We present a newly developed approach to reduce this instrument drift significantly, enabling flux measurements in natural environments. We have also developed the approach further to allow the matching of gas structures on vertical wall flight paths to sources and sinks in the footprint using an independent tracer of air movements across the scene. The new method produces drift-corrected simultaneous measurements of multiple GHGs (CO2, CH4, N2O) and wind data. We will present results using different flight strategies (e.g. single wall, two-wall, and rectangular walls) in both anthropogenic and natural environments.