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• 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]

• Cumulative and time-lag effects combined impact vegetation activities. • Solar radiation and temperature are the dominant climate factors in taiga and tundra. • The duration of temporal effects corresponds with ecosystem complexity. • The tundra converted to forests caused by warming enhanced the climate temporal effects. The Northern Hemisphere is experiencing amplified warming and greening; however, the responses of greening to warming remain unclear in terms of the cumulative and time-lag effects of climate factors. We chose Siberia, a hotspot in the Northern Hemisphere, as a study area and identified the relationships between the enhanced vegetation index (EVI) and climate factors (e.g., surface temperature, precipitation, and solar radiation) during 2000–2016. In the growing season, the average cumulative duration of the vegetation response to climate factors was 3.6 ± 2.0 months, and the time-lag period was 1.5 ± 1.2 months, with the longest duration in forests and the shortest duration in tundra. Solar radiation was the dominant driver of vegetation greening in 56% of the study area, followed by temperature (41%) and precipitation (3%). In addition, the temporal effects of the climate factors were separated through partial correlation analysis; vegetation responded most rapidly to solar radiation variations with a cumulative duration of 2.2 ± 1.2 months and a time-lag period of 1.8 ± 0.8 months, followed by temperature (2.3 ± 1.3 and 1.9 ± 1.0 months, respectively). Precipitation showed the longest cumulative effect (3.7 ± 1.5 months) and the shortest time-lag effect (1.2 ± 1.1 months). Our results suggest that vegetation activity is influenced by both cumulative and time-lag effects. The temporal effects vary among the land cover categories, and complex ecosystems generally correspond to long-term temporal effects, and vice versa. Thus, the conversion of tundra to boreal forest due to warming in Siberia enhances the temporal effects of climate. These temporal effects could be incorporated into Earth system models to improve our understanding and monitoring of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]