Satellite-Observed Greening of Human-Dominated Ecosystems in Global Dryland

Drylands play a critical role in the global carbon cycle. Recent studies have documented widespread dryland greening largely attributed to CO 2 fertilization, yet the role of human activities remained unclear. Here, we used satellite gross primary productivity (GPP) and state-of-the-art dynamic global vegetation models to quantify the global dryland productivity trend from 2001 to 2024 and determine the contributions of climatic and anthropogenic factors. Our results revealed that 29.20% of global drylands experienced significant greening; ( P value < 0.1) only 4.91% showed a significant browning trend. The robustness of the satellite GPP trend was corroborated by multiple lines of evidence from leaf area index, enhanced vegetation index, and machine-learning-based upscaling of in situ flux tower GPP (FLUXCOM-X). From 2001 to 2024, the global dryland GPP increased by 1,899 Tg C, with Asia contributing the largest share (983 Tg C). Partial least squares regression analysis revealed that human activities (effect size = 0.68) played a more important role than the CO 2 fertilization effect (0.32), with climate factors having only a minor role (0.03). Dynamic global vegetation models from the latest TRENDY v12 project substantially underestimated global dryland greening (multimodel ensemble mean GPP slope = 0.18 ± 2.45 g C m −2 y −1 versus mean satellite GPP slope = 7.66 ± 4.92 g C m −2 y −1 ) and overemphasized the role of CO 2 fertilization effect and climate. Our results therefore not only advanced our mechanic understanding of global dryland greening but also highlighted the urgent need for refined representation of human land use activities in Earth system models to accurately predict dryland ecosystem dynamics amid global warming and escalating freshwater demand.

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Publication Details

Journal
Journal of Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.34133/remotesensing.0941
Citations
3
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
3.32
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article

Satellite-Observed Greening of Human-Dominated Ecosystems in Global Dryland

Cao Chun-yan, Xuanlong Ma, Yi Li, Kai Yan et al.
3 citations
Journal of Remote Sensing
Land Use and Ecosystem Services
3.32
article

Satellite-Observed Greening of Human-Dominated Ecosystems in Global Dryland

Cao Chun-yan, Xuanlong Ma, Yi Li, Kai Yan, Kedi Liu, Xiaoyu Zhu
article en
3 citations

Abstract

Drylands play a critical role in the global carbon cycle. Recent studies have documented widespread dryland greening largely attributed to CO 2 fertilization, yet the role of human activities remained unclear. Here, we used satellite gross primary productivity (GPP) and state-of-the-art dynamic global vegetation models to quantify the global dryland productivity trend from 2001 to 2024 and determine the contributions of climatic and anthropogenic factors. Our results revealed that 29.20% of global drylands experienced significant greening; ( P value < 0.1) only 4.91% showed a significant browning trend. The robustness of the satellite GPP trend was corroborated by multiple lines of evidence from leaf area index, enhanced vegetation index, and machine-learning-based upscaling of in situ flux tower GPP (FLUXCOM-X). From 2001 to 2024, the global dryland GPP increased by 1,899 Tg C, with Asia contributing the largest share (983 Tg C). Partial least squares regression analysis revealed that human activities (effect size = 0.68) played a more important role than the CO 2 fertilization effect (0.32), with climate factors having only a minor role (0.03). Dynamic global vegetation models from the latest TRENDY v12 project substantially underestimated global dryland greening (multimodel ensemble mean GPP slope = 0.18 ± 2.45 g C m −2 y −1 versus mean satellite GPP slope = 7.66 ± 4.92 g C m −2 y −1 ) and overemphasized the role of CO 2 fertilization effect and climate. Our results therefore not only advanced our mechanic understanding of global dryland greening but also highlighted the urgent need for refined representation of human land use activities in Earth system models to accurately predict dryland ecosystem dynamics amid global warming and escalating freshwater demand.

Journal of Remote SensingVol. 5
Beijing Normal University (CN), State Key Laboratory of Remote Sensing Science (CN), Lanzhou University (CN)
Openalex Percentile: Top 7%
Land Use and Ecosystem Services
3.32
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