Impacts of Climate Change, Human Activities, and Their Interactions on China’s Gross Primary Productivity

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning.

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Publication Details

Journal
Remote Sensing
Published
2026-01-14
DOI
https://doi.org/10.3390/rs18020275
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Impacts of Climate Change, Human Activities, and Their Interactions on China’s Gross Primary Productivity

L. D. Shen, W. B. Fei, Jiabing Wu, Y. Liu et al.
Remote Sensing
Remote Sensing in Agriculture
article

Impacts of Climate Change, Human Activities, and Their Interactions on China’s Gross Primary Productivity

L. D. Shen, W. B. Fei, Jiabing Wu, Y. Liu, Rongrong Cai, Anzhi Wang, Lijun Huang, Yiwei Diao, Hao Zhou, Yuan Zhang, Jie Lai
article en

Abstract

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning.

Remote SensingVol. 18(2)
China Meteorological Administration (CN), National University of Defense Technology (CN), Chinese Academy of Sciences (CN), Institute of Applied Ecology (CN), Zhejiang Meteorological Bureau (CN), University of Chinese Academy of Sciences (CN)
Climate action
Openalex Percentile: Top 90%
Remote Sensing in Agriculture
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