A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m²/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

The surface water hydrochemistry of the Syr Darya River in Kazakhstan was investigated at 39 locations to analyze regional hydrochemical characteristics and evaluate the irrigation suitability of the studied regions. The cations in the surface water are mainly Na⁺, Ca²⁺, and Mg²⁺, while the anions are mainly SO₄²⁻. The main hydrochemical type is Ca-Mg-SO₄-Cl. From the perspective of natural factors, the hydrochemical characteristics in the study area are derived from the dual effects of rock weathering and evaporation-concentration; however, the influence of anthropogenic factors include industrial and agricultural production near the river and the inflow of urban domestic sewage on the hydrochemical characteristic is also present. The irrigation suitability evaluation of the surface water based on SAR, Na% and KI showed that the majority of the water is suitable for irrigation, and from the TDS content analysis, 71.43% of the samples are in a critical state, indicating that the influence of TDS concentration on irrigation suitability cannot be ignored. The results have practical significance for maintaining the sustainable use of water resources in the Syr Darya River.