Accurate real-time information about the spatial and temporal dynamics of soil salinization is crucial for preventing the aggravation of salinization and achieving sustainable development of the ecological environment. With the Bosten Lake watershed as the study area, in this study, the regional risk factors of soil salinization were identified, the salinization information was extracted, and the remote sensing-based ecological index (RSEI) of soil salinization was assessed through the combined use of remote sensing (RS) and geographic information system (GIS) techniques and measurements of soils samples collected from various field sites. The results revealed that (1) a four period (1990, 2000, 2010, and 2020) RS dataset on soil salinization allowed for the accurate classification of the land use/land cover types, with an overall classification accuracy of greater than 90% and kappa values of >0.90, and the salt index (SI), an RS-derived risk factor of soil salinization, was significantly correlated with the actual measured salt content of the surface soils. (2) The RS-derived elevation and normalized difference vegetation index (NDVI) were significantly correlated with the SI-T. (3) An integrated risk assessment model was constructed for the soil salinization risk in the Bosten Lake watershed, which calculated the integrated risk index values and classified them into four risk levels: low risk, medium risk, high risk, and extremely high risk. (4) Due to the combined effect of the surface water area and terrain, the soil salinization risk gradually decreased from the lake to the surrounding areas, while the corresponding spatial range increased in order of decreasing risk. The areas with different levels of soil salinization risk in the study area during the last 30 years were ranked in decreasing order of medium risk > high risk > extremely high risk > low risk. These findings provide theoretical support for preventing and controlling soil salinization and promoting agricultural production in the study area.

To estimate the potential risks of plant diversity reduction and soil salinization in the Bosten Lake Basin, the dynamic changes in the plant community and species diversity affected by soil moisture and salinity were analyzed from 2000 to 2020 based on remote sensing technology and field experiments. A model for simulating soil moisture, salinity, and the productivity of the plant communities was proposed. The results demonstrated that: (1) The soil moisture index (SMI) increased but the soil salinity index (SSI) decreased from 2000 to 2020 in the study areas. Accordingly, the plant community productivity indices, including the vegetation index (NDVI), enhanced vegetation index (EVI), and ratio vegetation index (RVI), exhibited an increasing trend. It was found that the Alpine meadow, Alpine steppe, and temperate steppe desert were the main types of plant communities in the study areas, accounting for 69% of its total area. (2) With increasing SMI or decreasing SSI, the vegetation productivity such as NDVI, RVI, and EVI all exhibited an increasing trend. With the increment of SMI, the species diversity indices of the Simpson, Shannon‒Wiener, and Margalef exhibited a distinctly increasing trend. However, the indices of the Simpson, Shannon‒Wiener, and Alatalo increased with the decreasing SSI. (3) The study discovered from the SVM model that the species diversity index was optimal when the soil salinity was 0–15 g/kg and the soil moisture was 12–30% in the study areas. It was found that soil moisture, not soil salinity, controls the plant species diversity change in the study areas. (4) A multiple linear regression model was established for simulating the effect of soil water-salinity on the vegetation productivity index at the watershed scale. The model indicated that higher salinity would reduce vegetation productivity and higher soil moisture would promote vegetation growth (except for RVI). The SSI had a higher impact on NDVI and EVI than the SMI in the study areas. This study would support decision-making on grassland ecosystem restoration and management in the other arid areas.

Low-cost and efficient dynamic monitoring of surface salinization information is critical in arid and semi-arid regions, we conducted a remote sensing inversion exercise for soil salinity in the Bosten Lake watershed in Xinjiang, Northwest China, with a total area of about 43,930 km2, a typical watershed in an arid area. Sentinel MSI and Landsat OLI data were combined with measured soil salinity data in July 2020, and optimal combination bands were selected based on characteristic bands to create a grid search-support vector machine (GS-SVM) inversion model of soil salt content. The maximum value of soil salt content in the Bosten Lake watershed was 11.8 g/kg. The minimum value was 0.41 g/kg, and the average value was 4.77 g/kg, soil salinization is serious. The results of previous studies were applied to the estimation of salt content in Bosten Lake watershed and could not meet the monitoring requirements of the study area, R2 < 0.3. The GS-SVM soil salinity monitoring model was established based on the optimal DI, RI, and NDI remote sensing indexes for the Bosten Lake watershed. After model verification, it was found that the optimal model of image data was the Landsat OLI first-derivative model with R2 of 0.64, RMSE of 3.12, and RPD of 1.64, indicating that the prediction ability of the model was high. We used the first-order derivative model of Landsat OLI data to map the soil salt content in the Bosten Lake watershed in arid area, and found that soil salt content in most of the study area was between 10 and 20 g/kg, indicating severe salinization. This study not only reveals the distribution characteristics of salinization in Bosten Lake watershed, but also provides a scientific basis for soil salinization monitoring in Central Asia to lay a foundation for further soil salinization monitoring in arid areas.