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

• Main Authors: Lourenço, Ricardo Barros, Smith, Michael J, Smullin, Sylvia, Jain, Umangi, Gonsamo, Alemu, Ouaknine, Arthur
• Format: Journal Article
• Language: English
• Published: 28.10.2023
• Subjects: Physics - Atmospheric and Oceanic Physics; Physics - Geophysics
• DOI: 10.48550/arxiv.2310.18703

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.