The spatio – temporal variation characteristics and attributions of vegetation net primary productivity in the Qinling Mountains, China

Abstract Monitoring spatial – temporal variations of vegetation NPP (Net Primary Productivity) and understanding its driving factors is essential for achieving carbon neutrality and mitigating climate change. This study investigates the spatio – temporal variations and key influencing factors of vegetation NPP in the Qinling Mountains from 2001 to 2023, using the Geographic Detector Model, Theil – Sen Median, Mann – Kendall test, and Hurst index. The analysis based on MODIS NPP MOD17A3HGF v006 series products and incorporates environmental variables such as 10 m wind speed (vs), reference evapotranspiration (pet), maximum temperature (tmx), downward surface shortwave radiation (srad), Palmer Drought Severity Index (PDSI), soil moisture (soil), average temperature (tem), precipitation (pr) and Land use/cover change (LUCC). The results indicated that vegetation NPP in the Qinling Mountains showed an increasing trend with a slope of 5.74 × 10 −3 gC/m 2 yr from 2001 to 2023. Spatially, the vegetation NPP follows a spatial distribution pattern of “high in the east and low in the west”, with an average value of 629.89 gC/m 2 yr. Over the past 23 years, vegetation NPP has demonstrated a significant increase, covering 77.71 % of the study area. The results of geographic detector factor detection show that among the nine influencing factors, LUCC has the greatest impact on vegetation NPP, followed by temperature, wind speed, evapotranspiration, maximum temperature, Palmer Drought Severity Index, radiation, and precipitation, while soil moisture has the smallest impact. In addition, the results also indicate a strong interaction between LUCC and temperature, as well as precipitation and temperature, on NPP in the Qinling Mountains. The Hurst index indicated a weak downward trend in future vegetation NPP, affecting 81.98 % of the study area.