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.
@article{arxiv.2310.18703,
title = {Detangling the role of climate in vegetation productivity with an explainable convolutional neural network},
author = {Ricardo Barros Lourenço and Michael J. Smith and Sylvia Smullin and Umangi Jain and Alemu Gonsamo and Arthur Ouaknine},
journal= {arXiv preprint arXiv:2310.18703},
year = {2023}
}
Comments
7 pages, 2 figures, submitted to Tackling Climate Change with Machine Learning at NeurIPS 2023