EGU24-12579, updated on 10 Apr 2024
https://doi.org/10.5194/egusphere-egu24-12579
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Eddy Covariance and Artificial Intelligence: a review

Arianna Lucarini1,2, Mauro Lo Cascio2,3, Serena Marras2,3, Donatella Spano2,3, and Costantino Sirca2,3
Arianna Lucarini et al.
  • 1University School for Advanced Studies IUSS Pavia, Pavia, Italia
  • 2Department of Agricultural Sciences, University of Sassari, Sassari, Italy
  • 3CMCC - Euro-Mediterranean Centre on Climate Change Foundation, Sassari, Italy

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.

How to cite: Lucarini, A., Lo Cascio, M., Marras, S., Spano, D., and Sirca, C.: Eddy Covariance and Artificial Intelligence: a review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12579, https://doi.org/10.5194/egusphere-egu24-12579, 2024.

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