As a critical ecological barrier in the arid and semi-arid regions of northwestern China, the spatio-temporal evolution of vegetation carbon sequestration in the Hexi Corridor is of great significance to the ecological security of this region. Based on multi-source remote sensing and meteorological data, this study integrated second-order partial correlation analysis, ridge regression, and other methods to reveal the spatio-temporal evolution patterns of Gross Primary Productivity (GPP) in the Hexi Corridor from 2003 to 2022, as well as the response characteristics of GPP to air temperature, precipitation, and Vapor Pressure Deficit (VPD). From 2003 to 2022, GPP in the Hexi Corridor showed an overall increasing trend, the spatial distribution of GPP showed a pattern of being higher in the east and lower in the west. In the central oasis region, intensive irrigation agriculture supported consistently high GPP values with sustained growth. Elevated air temperatures extended the growing season, further promoting GPP growth. Due to irrigation and sufficient soil moisture, the contributions of precipitation and VPD were relatively low. In contrast, desert and high-altitude permafrost areas, constrained by water and heat limitations, exhibited consistently low GPP values, which further declined due to climate fluctuations. In desert regions, high air temperatures intensified evaporation, suppressing GPP, while precipitation and VPD played more significant roles. This study provides a detailed analysis of the spatio-temporal change patterns of GPP in the Hexi Corridor and its response to climatic factors. In the future, the Hexi Corridor needs to adopt dual approaches of natural restoration and precise regulation, coordinate ecological security, food security, and economic development, and provide a scientific paradigm for carbon neutrality and ecological barrier construction in arid areas of Northwest China.

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 degrees C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.