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
- DOI
- 10.1186/s13021-026-00487-5
- Published
- 2026-07-26
- Container
- Carbon Balance and Management
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13021-026-00487-5,
title = {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},
author = {Huiwen Li and Yue Cao and Jingfeng Xiao and Fei Yu and Rui Wang and Xinyi Hao and Zuoqiang Yuan},
year = {2026},
journal = {Carbon Balance and Management},
doi = {10.1186/s13021-026-00487-5},
url = {https://doi.org/10.1186/s13021-026-00487-5}
}RIS
TY - JOUR TI - 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 AU - Huiwen Li AU - Yue Cao AU - Jingfeng Xiao AU - Fei Yu AU - Rui Wang AU - Xinyi Hao AU - Zuoqiang Yuan PY - 2026 JO - Carbon Balance and Management DO - 10.1186/s13021-026-00487-5 UR - https://doi.org/10.1186/s13021-026-00487-5 ER -
APA
Li, H., Cao, Y., Xiao, J., Yu, F., Wang, R., Hao, X., & Yuan, Z. (2026). 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. Carbon Balance and Management. https://doi.org/10.1186/s13021-026-00487-5
Source records
- crossref · retrieved 2026-11-11T01:00:56.201Z