Analysis on spatiotemporal heterogeneity and impact mechanism of carbon sink in Qinling Mountains based on leaf area index

Accurately understanding the driving mechanisms of ecosystem carbon sinks in climate-sensitive regions is crucial for achieving carbon neutrality goals. Using the Qinling Mountains as a case study, this research aims to deconstruct the complex effects of environmental factors on carbon sinks, quantified in this study as Net Ecosystem Productivity (NEP), and assess the central mediating role of the Leaf Area Index (LAI). By integrating multi-source remote sensing data from 2001 to 2020, we employed spatiotemporal analyses and Structural Equation Modeling (SEM) to examine the direct and mediated impact pathways of various driving factors. The results reveal that: (1) LAI and NEP in the Qinling Mountains showed highly coupled spatial patterns over the past two decades, with 90.6% and 69.4% of pixels exhibiting significant increases respectively ( p < 0 . 05 ). (2) SEM analysis confirmed that LAI serves as the strongest direct positive driver of NEP ( β = 0 . 650 ) and plays a critical mediating role in transmitting environmental factor effects to NEP. (3) Topography emerged as the primary influence on LAI distribution. Elevation exhibited a significant negative effect on LAI, while slope demonstrated a dual positive effect—both a direct and an LAI-mediated effect. The final model successfully explained 77.1% of the variance in NEP and 69.5% of the variance in LAI. This study establishes a comprehensive driving framework of “Environmental Factors → Vegetation Canopy (LAI) → NEP (Carbon sink)”, demonstrating that enhancing LAI through scientific management and ecological restoration represents the most direct and effective strategy for maximizing the carbon sink function of mountain ecosystems. • Leaf Area Index (LAI) and carbon sink (NEP) showed highly coupled increasing trends. • LAI is the strongest direct positive driver of the ecosystem carbon sink (NEP). • Topography is the primary factor controlling LAI distribution, indirectly driving NEP. • A clear mechanistic framework ‘Environmental Factors → LAI → NEP’ is established.