(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R² = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.

The evaluation of landscape ecological risk (LER) in a river basin holds significant importance for the overall ecological environmental protection of the basin and subsequent sustainable development. The Shiyang River basin, a typical arid inland river basin, was selected as the research object, and the optimal scale of LER research was explored. Multi-period land use data was used to build an LER assessment model and reveal the temporal and spatial changes of LER in the Shiyang River basin. The PLUS model was used to simulate the spatial distribution characteristics and change trends of LER under different scenarios in 2030. The results show that the LER in the Shiyang River Basin has obvious scale dependence, with optimal granularity and magnitude of 60 m and 4.5 km, respectively. LER is dominated by higher risk and high risk categories, with significant spatial differences, showing a trend of low in the southwest and high in the northeast. The LER of the Shiyang River Basin decreased from 2000 to 2020. It is expected that the LER value under different scenarios in 2030 will show a downward trend, and the LER value under the ecological conservation priority scenario will be the lowest. This study can provide a reference for LER assessment in arid inland river basins.