# Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022
###### Abstract  
Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 \(\times\) 10\({}^{-3}\) yr\({}^{-1}\) (\(p<\) 0.01), 1.30 g C m\({}^{-2}\) yr\({}^{-2}\) (\(p<\) 0.01) and 1.00 \(\times\) 10\({}^{-3}\) Wm\({}^{-2}\)um\({}^{-1}\)sr\({}^{-1}\)/yr\({}^{-1}\) (\(p<\) 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia. vegetation coverage change; natural change; human activities; residual trend analysis 2020 xx 132022. 2020 xxMonitoring vegetation activity through remote sensing and analyzing its relationships with natural changes has become a crucial research field on a global or a specific regional scale in recent years [7; 8; 9; 10]. The normalized vegetation index (NDVI), derived from infrared and near-infrared remote sensing data, is an excellent indicator widely applied in studying vegetation activity on a global or a specific regional scale, reflecting vegetation greenness effectively [11]. However, in specific situations, such as under severe drought conditions or in areas with low vegetation cover, the accuracy of NDVI in reflecting vegetation activity is limited [12]. The gross primary productivity (GPP) of terrestrial ecosystems, signifying the total organic carbon assimilated by vegetation through photosynthesis per unit of time and area, constitutes the largest carbon flux in the carbon budget and plays a fundamental role in the carbon cycle [13]. Quantifying terrestrial gross primary productivity (GPP) and its changes is crucial for understanding the dynamic characteristics of global and regional vegetation productivity [14; 15]. In addition, compared to the traditional vegetation greenness and productivity index (NDVI and GPP), solar-induced chlorophyll fluorescence (SIF) is a newly remote sensing satellite dataset and is known to be an effective probe for vegetation photosynthesis [16; 17]. Many scholars have used the SIF dataset to analyze vegetation cover changes in regions [18; 19; 20; 21]. In summary, NDVI, GPP and SIF can all, to a certain extent, reflect vegetation activity, but each indicator emphasizes different aspects. Therefore, for a comprehensive understanding of vegetation activity in the Eurasian drylands, it is necessary to conduct a comprehensive and systematic analysis using multiple vegetation-related indicators. Hydrothermal factors are the most crucial natural indicators influencing vegetation activity [22; 23; 24]. Previous studies have highlighted that increased precipitation plays a significant role in promoting vegetation greening, especially in global drylands.