Using reinforcement learning to improve drone-based inference of
  greenhouse gas fluxes
Alouette van Hove Kristoffer Aalstad Norbert Pirk
Accurate mapping of greenhouse gas fluxes at the Earth's surface is essential
for the validation and calibration of climate models. In this study, we present
a framework for surface flux estimation with drones. Our approach uses data
assimilation (DA) to infer fluxes from drone-based observations, and
reinforcement learning (RL) to optimize the drone's sampling strategy. Herein,
we demonstrate that a RL-trained drone can quantify a CO2 hotspot more
accurately than a drone sampling along a predefined flight path that traverses
the emission plume. We find that information-based reward functions can match
the performance of an error-based reward function that quantifies the
difference between the estimated surface flux and the true value. Reward
functions based on information gain and information entropy can motivate
actions that increase the drone's confidence in its updated belief, without
requiring knowledge of the true surface flux. These findings provide valuable
insights for further development of the framework for the mapping of more
complex surface flux fields.