Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf...

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Bibliographic Details
Published inarXiv.org
Main Authors Ricardo Barros Lourenço, Smith, Michael J, Smullin, Sylvia, Jain, Umangi, Gonsamo, Alemu, Ouaknine, Arthur
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.10.2023
Subjects
Artificial neural networks
Biodiversity
Carbon
Leaf area index
Productivity
Quality standards
Vegetation index
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Summary:Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity.
ISSN:2331-8422