Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R² of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R² being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.

In order to study the root–soil composite system shear characteristics under the action of freeze–thaw cycles in the permafrost regions along the Qinghai–Tibet Highway (QTH) from the Beiluhe–Tuotuohe (B-T) section, the slopes in the permafrost regions along the QTH from the B-T section were selected as the object of the study. The direct shear test of root–soil composite systems under different amounts of freeze–thaw (F-T) cycles and gray correlations were used to analyze the correlation between the number of F-T cycles, water content, root content, and the soil shear strength index. The results show that the cohesion of the soil in the area after F-T cycles exhibits a significant stepwise decrease with an increase in F-T cycles, which can be divided into three stages: the instantaneous stage (a decrease of 46.73–56.42%), the gradual stage (a decrease of 14.80–25.55%), and the stabilization stage (a decrease of 0.61–2.99%). The internal friction angle did not exhibit a regular change. The root–soil composite system showed significant enhancement of soil cohesion compared with soil without roots, with a root content of 0.03 g/cm³ having the most significant effect on soil cohesion (increasing amplitude 65.20–16.82%). With an increase in the number of the F-T cycles, while the water content is greater than 15.0%, the greater the water content of the soil, the smaller its cohesion becomes. Through gray correlation analysis, it was found that the correlation between the number of F-T cycles, water content, root content, and soil cohesion after F-T cycles were 0.63, 0.72, and 0.66, respectively, indicating that water content had the most significant impact on soil cohesion after F-T cycles. The results of this study provide theoretical support for further understanding the variation law of the shear strength of root–soil composite systems in permafrost regions under F-T cycles and the influencing factors of plant roots to enhance soil shear strength under F-T cycles, as well as for the scientific and effective prevention and control of retrogressive thaw slump in the study area, the QTH stretches across the region.