Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2024
Research article |
| 13 Nov 2024
| 13 Nov 2024
Learning extreme vegetation response to climate drivers with recurrent neural networks
Francesco Martinuzzi
CORRESPONDING AUTHOR
Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
Institute for Earth System Science & Remote Sensing, Leipzig University, Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University and UFZ, Leipzig, Germany
Miguel D. Mahecha
Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
Institute for Earth System Science & Remote Sensing, Leipzig University, Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University and UFZ, Leipzig, Germany
German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany
Gustau Camps-Valls
Image Processing Laboratory (IPL), Universitat de València, València, Spain
David Montero
Institute for Earth System Science & Remote Sensing, Leipzig University, Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University and UFZ, Leipzig, Germany
German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany
Tristan Williams
Image Processing Laboratory (IPL), Universitat de València, València, Spain
Karin Mora
Institute for Earth System Science & Remote Sensing, Leipzig University, Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University and UFZ, Leipzig, Germany
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Short summary
We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
We investigated how machine learning can forecast extreme vegetation responses to weather....
