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

Autor:	Lourenço, Ricardo Barros, Smith, Michael J., Smullin, Sylvia, Jain, Umangi, Gonsamo, Alemu, Ouaknine, Arthur
Rok vydání:	2023
Předmět:	Physics - Atmospheric and Oceanic Physics Physics - Geophysics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 2 figures, submitted to Tackling Climate Change with Machine Learning at NeurIPS 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.18703 Zobrazit plný text záznamu View this record from Arxiv