Since the 21st century, China has experienced rapid development, and the spatial and temporal changes in vegetation cover have become increasingly significant. Southern China is a representative region for human activities, climate change, and vegetation change, but the current human understanding of the interactions between vegetation and its influencing factors is still very limited. In our study, we use NDVI as the vegetation greenness data, land cover data, temperature, precipitation, downgradient shortwave radiation, and ${\mathrm{CO}}_2$ data to investigate the interrelationship among vegetation, climate change, and human activities in southern China. The changes and their consistency were studied by trend analysis and Hurst exponent analysis. Then, the contribution of each influencing factor from 2001 to 2020 was quantified by random forest. The results showed that the vegetation in southern China showed an overall rising trend, and areas with a continuous changing trend were concentrated in the Pearl River Delta, western Guangdong, and eastern Guangdong, with a growth rate of 0.02∼0.04%. The vegetation in northern Guangdong did not change significantly. The main factor of NDVI spatial variation in southern China is the land-use factor, accounting for 79.4% of the variation, while climate factors produce further differences. The contributions and lagged effects of NDVI factors on different land-use types and the lagged effects of different climate factors are different and are related to the climate and vegetation background in Sourthern China. Our study is useful in estimating the contribution of NDVI change by each considered factor and formulating environmentally friendly regional development strategies and promoting human–land harmony.

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Vegetation net primary productivity (NPP) is a key indicator for assessing vegetation dynamics and carbon cycle balance. Xinjiang is located in an arid and ecologically fragile region in northwest China, but the current understanding of vegetation dynamics in the region is still limited. This study aims to analyze Xinjiang’s NPP spatial and temporal trends, using random forest regression to quantify the extent to which climate change and human activities affect vegetation productivity. CMIP6 (Coupled Model Intercomparison Project Phase 6) climate scenario data help assess vegetation restoration potential and future risks. Our findings indicate that (1) Xinjiang’s NPP exhibits a significant increasing trend from 2001 to 2020, with three-quarters of the region experiencing an increase, 2.64% of the area showing significant decrease (p < 0.05), and the Ili River Basin showing a nonsignificant decreasing trend; (2) precipitation and radiation are major drivers of NPP variations, with contribution ratios of 35.13% and 30.17%, respectively; (3) noteworthy restoration potential exists on the Tian Shan northern slope and the Irtysh River Basin, where average restoration potentials surpass 80% relative to 2020, while the Ili River Basin has the highest future risk. This study explores the factors influencing the current vegetation dynamics in Xinjiang, aiming to provide references for vegetation restoration and future risk mitigation, thereby promoting sustainable ecological development in Xinjiang.