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2026年2期
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Estimating CO2 fluxes through integrating spatial and temporal input layers via deep learning algorithms
Estimating CO2 fluxes through integrating spatial and temporal input layers via deep learning algorithms
摘要
Background Accurate estimation of net ecosystem exchange of CO2 fluxes(Fc)is essential for understanding car-bon cycle processes and assessing ecosystem carbon budgets.However,conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance(EC)towers,potentially limiting predictive accuracy and interpretability of Fc estimates.To address this challenge,we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial informa-tion with sequential environmental drivers.
Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions.Using multi-year flux tower observations,remote sensing vegetation indices and footprint modeling,we evaluate the proposed method across three land cover types.This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines,achieving the highest overall accuracy(R2=0.9569)and the lowest RMSE(1.8128 μmol m-2 s-1)and MAE(1.1939 μmol m-2 s-1).Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity,where spatial structure substantially modulates Fc variability.
Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems,supporting refined assessments of terrestrial carbon fluxes,and enhancing scientific foun-dations for carbon studies.
年，卷(期)：
2026, 15(2)
机标分类号：
H3.1
G43
R338.1
在线出版日期：
2026年7月16日
