Eddy Covariance and Artificial Intelligence: a review

The Eddy Covariance (EC) method allows for the monitoring of carbon, water, and energy fluxes between Earth’s surface and atmosphere. Due to it’s varying interdependent data streams and abundance of data as a whole, EC is naturally suited to Artificial Intelligence (AI) approaches. The integration of AI and EC will likely play a crucial role in the climate change mitigation and adaptation goals defined in the Sustainable Development Goals (SDGs) of the Agenda 2030.

To aid this, we present a scoping review in which the novelty of various AI techniques in environmental science from the past two decades has been collected. Overall, we find a clear positive trend in the quantity of research in this area, particularly in the last five years. We also find a lack of uniformity in available techniques, due to the diverse technologies and variables employed across environmental conditions and ecosystems.

We suggest that future progress in this field requires an international, collaborative effort involing computer scientists and ecologists. Modern DL techniques such as Transformers and generative AI must be investigated to find how they may benefit our field. A forward-looking strategy must be formed for the optimal utilization of AI combined with EC to define the future actions in flux monitoring in the face of climate change.

 

Keywords: eddy covariance, artificial intelligence, flux monitoring, machine learning, deep learning, climate change.