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  5. Persistent vegetation greening trends across China’s wetlands

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Article
en
2025

Persistent vegetation greening trends across China’s wetlands

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en
2025
Vol 6 (1)
Vol. 6
DOI: 10.1038/s43247-025-02628-z

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Pete Smith
Pete Smith

University of Aberdeen

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Yongxing Ren
Dehua Mao
Tao Wang
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Abstract

Abstract Vegetation is the basic component of wetland ecosystems. Monitoring changes in aboveground biomass (AGB) of wetland vegetation is crucial for understanding the response of wetland ecosystems to global climate change. China has vast wetlands experiencing diverse climate change impacts. However, how the AGB of wetland vegetation responds to climate change and human impacts remains unclear. This lack of understanding stems from insufficient in-situ observations, complex interactions between wetland vegetation growth and local hydrology, and challenges in estimating AGB using remote sensing data alone. Here, we compiled a wetland AGB dataset with 1087 sites covering all wetland regions in China (639 sample points from field sampling and 448 sample points from literature). Based on this dataset, we mapped the spatial distribution of wetland AGB in China using machine learning algorithms to understand its historical and future changes. The wetland AGB density of China in 2020 was 352.23 ± 32.67 g C m−2 on average and the total wetland AGB stock was 57.51 ± 6.36 Tg C. During the past two decades (2000–2023), wetland AGB has gradually increased, indicating a notable greening trend in China’s wetlands. Our results project that China’s wetlands could continue to green-up rapidly and sustainably under various future climate change scenarios, but with varying degrees of greening. This highlights the difference in wetland ecosystem response to various climate conditions.

How to cite this publication

Yongxing Ren, Dehua Mao, Tao Wang, Mohamed Abdalla, Pete Smith, Xiangming Xiao, Zicheng Yu, Xiuxue Chen, Yanbiao Xi, Ling Luo, Xiaoyan Li, Zongming Wang (2025). Persistent vegetation greening trends across China’s wetlands. , 6(1), DOI: https://doi.org/10.1038/s43247-025-02628-z.

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Publication Details

Type

Article

Year

2025

Authors

12

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1038/s43247-025-02628-z

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