Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles

Accurately quantifying total greenhouse gas emissions (e.g., methane) from natural systems such as lakes, reservoirs, and wetlands requires the spatial and temporal measurement of both diffusive and ebullitive (bubbling) emissions. Ebullitive emissions exhibit high spatial and temporal variability and as such are difficult to measure. Traditional manual measurement techniques provide only limited localized assessment of methane flux, often introducing significant errors when extrapolated to the whole‐of‐system. This is further exacerbated when whole‐of‐region estimates are developed for inclusion in global greenhouse gas inventories. In this paper, we directly address these current sampling limitations by comparing two robot boat‐based sampling systems with complementary sensing modalities to directly measure in real time the spatiotemporal release of methane to atmosphere across inland waterways. The first system consists of a single Autonomous Surface Vehicle (ASV) fitted with an Optical Methane Detector with algorithms to exploit the robot's mobility and transect repeatability for the accurate detection and quantification of methane bubbles across whole‐of‐system. The second sys