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Leveraging meteorological legacy effects and machine learning to improve daily gap-free normalized difference vegetation index reconstruction: a critical basis for fine-scale carbon sink capacity monitoring

Crossref DOI link: https://doi.org/10.1186/s13021-026-00487-5

Published Online: 2026-07-26

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Li, Huiwen

Cao, Yue

Xiao, Jingfeng

Yu, Fei

Wang, Rui

Hao, Xinyi

Yuan, Zuoqiang
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (42501375)

National Natural Science Foundation of China (32571823)
License Information

Text and Data Mining valid from 2026-07-26

Accepted Manuscript valid from 2026-07-26
More Information

Article History

Received: 28 February 2026

Accepted: 14 July 2026

First Online: 26 July 2026

Declarations

Ethics approval and consent to participate: This study conforms to the norms of academic ethics and morality.

Consent for publication: All authors have approved the manuscript and agree with its submission to Carbon Balance and Management.

Competing interests: The authors declare no competing interests.

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