Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China

Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China

Abstract As a key ecological parameter, NPP measures the photosynthetic efficiency of plants in capturing atmospheric carbon. With the warming of the climate, extreme temperature events are frequent, which has exerted a profound influence on NPP. Previous studies on the drivers of NPP have predomina...

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Bibliographic Details
Main Authors: Dewei Xie, Zhaopei Zheng, Xin Ding, Lihong Wei, Yu Lan
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Extreme temperature events
Net primary productivity (NPP)
Spatiotemporal variation
Deep learning
SHAP method
Online Access:https://doi.org/10.1038/s41598-025-16613-1
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https://doi.org/10.1038/s41598-025-16613-1

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