(1) Background: Recently, secondary soil salinization has frequently occurred in ecological water conveyance and irrigation areas. Therefore, monitoring the information on soil salinization in the conveyance irrigation area and analyzing the change process of soil salinization are of great significance to both environmental improvement and salinization management. (2) Methods: This study takes the Shiyang River’s terminal lake—Qingtu Lake—and its surroundings as the research area. The salinization index was extracted by remote sensing data and methods, and a comprehensive salinization model (CSI) was established. Firstly, the spatial distribution regular of soil salinization, since ecological water conveyance was explored, and the change trends in soil salinization were analyzed. Secondly, the relationship between the change characteristics of soil salinization and groundwater, water area changes and land use types were analyzed. (3) Results: The Kappa coefficient between the soil salinization extracted by CSI and the measured data reached 0.703, and CSI can accurately monitor soil salinization information. From 2011 to 2020, the non-salinized land increased by 2.1%, mild and moderate salinization decreased by 5.7% and 3.9%, respectively, and severe salinization increased by 7.5%. The salinization in the lake area showed a decreasing trend, and the periphery of the lake area showed an increasing trend. Groundwater, water area changes and land use type have certain influences on soil salinization in the study area. (4) Conclusion: Since ecological water conveyance, the total proportion of soil salinized area in Qingtu Lake and its surrounding areas has not changed much, but there are certain changes between different grades of salinized land. The areas with changes to the level of salinization are mainly shallow, unstable water areas, saline–alkali land, wetlands and sandy land, and there are secondary salinization problems in these areas.

(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.

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.

The time series study of glacier movement is of special importance for rational management of freshwater resources, studying glacier evolution, understanding mechanism of glacier movement, and assessing disasters caused by glacier movement. In this paper, we put forward an optimization scheme for the shortcomings in the calculation method of using remote sensing to invert the three-dimensional (3D) surface motion displacement of glacier. The optimized method consists of Offset Tracking method, Optimizing the offset tracking results by means of iterative filtering, OT-SBAS technology and Conversion of 3D surface motion displacement of glacier. The Urumqi Glacier No. 1 was selected to test the optimized method. The 3D surface motion displacement of Urumqi Glacier No. 1 was retrieved by using the optimized method based on the ascending and descending Sentinel-1 datasets from 19 April to 29 August 2018. The distribution of 3D surface velocity of the Urumqi Glacier No. 1 was obtained in time series, and the accuracy of the inversion results was evaluated by using the field measurement data. The results show that the accuracies of the inverted displacements of east branch of Urumqi Glacier No. 1 (UG1E) were about 0.062, 0.063, and 0.152 m in the east, north and vertical directions, and these values for the west branch (UG1W) were 0.015, 0.020 and 0.026 m, respectively. It is indicated that using Sentinel-1 ascending and descending data and using the optimized method to retrieve the 3D surface motion displacement of glacier should satisfy the requirements of inversing the 3D surface motion displacement of high-latitude mountain glaciers in China.

Remote sensing estimations of glacier flow velocity could provide effective methods for the long-term monitoring of glacier flow velocity. This paper calculated the velocity in the line-of-sight (LOS) direction by combining DInSAR and offset-tracking technology with ascending and descending Sentinel-1 images of the Urumqi Glacier No.1 from 2016 to 2017. Meanwhile, the velocity in the azimuthal direction was obtained by combining MAI and offset-tracking technology. Then, the eastward, northward, and upward flow velocities were retrieved using the Helmert variance component estimation method. Finally, the standard error of the mean and mean errors of surface velocity in non-glaciated areas of the Urumqi Glacier No.1 were calculated to evaluate the accuracy of the results generated by the proposed method. The results showed: (1) The ascending LOS velocity and the descending LOS velocity were 1.812 m/a and −1.558 m/a from 2016 to 2017. The ascending azimuthal and descending azimuthal velocities were 0.978 m/a and −2.542 m/a, respectively. (2) The glacier flow velocities were 2.571 m/a and 1.801 m/a, respectively, for the eastward and northward directions. In the vertical direction, the velocity was −0.554 m/a. (3) The accuracy of the results generated by the proposed method were 0.028 m/a, 0.085 m/a, and 0.063 m/a in the east, north, and vertical directions. Therefore, it is suitable to use ascending and descending Sentinel-1 images and the study method proposed in this paper to estimate the surface flow velocity of mountain glaciers.