Soil salinization significantly jeopardizes agricultural productivity and ecological stability in southern Xinjiang’s oasis regions, highlighting the urgent need to examine its spatial–temporal trends and driving mechanisms for improved resource management. Utilizing soil salinity measurements collected in 2010 and 2023, the current research applied multiple environmental variables processed via the Google Earth Engine (GEE) platform to evaluate the predictive capability of four machine learning algorithms—random forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Tree (CART), and Support Vector Machine (SVM)—for accurate large-scale salinity mapping. Subsequently, a piecewise structural equation model (piecewiseSEM) was employed to quantitatively analyze the driving factors of soil salinization. Correlation analysis revealed seven critical variables—Red, NDSI, kNDVI, SDI, ET, elevation, and SM—as the most influential among the 41 environmental factors assessed for their impact on soil salinity. The performance evaluation ranked the models as follows: RF > GBDT > SVM > CART, with RF achieving the highest predictive accuracy (R² = 0.756, RMSE = 2.265 g·kg⁻¹, MAE = 1.468 g·kg⁻¹). Between 2010 and 2023, soil salinization severity in the region exhibited a slight overall decrease; however, the extent of this reduction was relatively modest. The proportion of moderately and severely salinized areas declined, accompanied by reduced spatial variability, whereas the extent of mildly salinized soils increased markedly. These findings imply that soil salinity primarily experiences internal redistribution within the surface layers, with limited downward leaching. Evapotranspiration (ET) and soil moisture (SM) were identified as the dominant drivers affecting salinity dynamics during both periods, with the influence of SM becoming more pronounced over time. This trend highlights that in conditions of limited natural variability, human-induced irrigation practices have emerged as the primary regulator of soil salinity levels. The findings of this study provide novel methodologies and data support for the monitoring and prevention of soil salinization in arid regions.

This study focuses on the ecological vulnerability and its driving mechanism of the Ebinur Lake Basin. Integrating natural factors such as annual average temperature, annual precipitation and elevation, as well as social factors including GDP and population distribution, it systematically evaluates the ecological vulnerability of the basin from 1994 to 2024 by adopting methods like the SRP model, Analytic Hierarchy Process (AHP) and Geodetector. The results show that the overall scale of ecologically vulnerable areas in the basin has presented a shrinking trend over the past 30 years: the area of severe vulnerability reached a peak of 14,270.31 square kilometers in 2004 and then decreased to 13,242.39 square kilometers; the area of slight vulnerability increased by 60.8%; and the proportion of moderate vulnerability has slightly risen since 2014. Spatially, the vulnerability exhibits significant agglomeration characteristics: severe vulnerable areas are concentrated in the mountainous areas of the basin boundary and the eastern region of Ebinur Lake, while slight vulnerable areas are distributed in woodlands and farmlands of alluvial fans in low mountains and hills. Geodetector analysis shows that, fractional vegetation cover, normalized difference vegetation index and land use type are the dominant factors, natural factors and social factors interact significantly.This study provides a scientific basis for ecological protection and sustainable development of the basin.