Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-535-2024
https://doi.org/10.5194/npg-31-535-2024
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

Learning extreme vegetation response to climate drivers with recurrent neural networks

Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora

Data sets

Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set (https://meta.icos-cp.eu/collections/tEAkpU6UduMMONrFyym5-tUW) S. Walther et al. https://doi.org/10.5194/bg-19-2805-2022

Model code and software

rnn-ndvi Francesco Martinuzzi https://github.com/MartinuzziFrancesco/rnn-ndvi

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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.