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...
Saved in:
Main Authors | , , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
28.10.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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. |
---|---|
DOI: | 10.48550/arxiv.2310.18703 |