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[Display omitted] • The proportion of vegetation improvement area in QLMs reached 65.06% in the past 21 years, while 52% of the area will be continuously improved in the future. • The distribution of the NDVI in the QLMs maintained a high spatial agglomeration. • Annual sunshine duration is the main meteorological factor affecting vegetation change in Qilian Mountains. • The interaction between different driving factors can improve the explanatory power of vegetation changes compared with a single driving factor. Vegetation is an important indicator reflecting ecological environment stability, and monitoring vegetation change and understanding its potential driving force have important guiding significance for the adjustment and implementation of ecological restoration measures. As an ecological security barrier in northwest China, the Qilian Mountains (QLMs) play an important role in promoting the green development of social and economic development. Currently, the comprehensive driving effects of natural and anthropogenic factors on vegetation change in the QLMs are not clear. Based on the normalized difference vegetation index (NDVI), the spatiotemporal characteristics and trend changes in vegetation in the QLMs were systematically analyzed from 2000 to 2020 in this paper. The effects of natural and anthropogenic driving factors on vegetation change were explored using a geographic detector (GeoDetector). The results showed that the vegetation has improved continuously in the past 21 years, but the overall vegetation coverage was still low. The vegetation distribution was highly clustered, with a decreasing trend from east to west. Annual sunshine duration (q statistic = 0.3347) and distance to the rivers (q statistic = 0.2649) had the greatest explanatory power for vegetation change, while slope, aspect, and landform type had the least explanatory power. The interaction between elevation and sunshine duration, temperature and precipitation, temperature and sunshine duration, elevation and precipitation had the most explanatory power for vegetation change. Finally, we determined the ranges or categories of driving factors that were most suitable for vegetation growth by using a risk detector. The results of this study can help us further understand the potential driving mechanism of vegetation coverage variation in the QLMs and provide theoretical guidance for relevant managers to formulate ecological restoration measures and land management policies in the next step. It is of great significance to maintain the stability of the fragile ecological environment and prevent land degradation in arid and semiarid areas of Northwest China. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]