The accurate high-resolution mapping of saturated soil hydraulic conductivity (Ks) is crucial for the advancement of hydrological modeling and soil-water management practices. Traditional approaches, including pedo-transfer functions (PTFs) and coarse-resolution digital soil mapping, encounter constraints in delineating fine-scale spatial heterogeneity. This is attributed to their dependence on sparse soil properties or low-resolution environmental covariates. This study proposes a novel approach that integrates multi-sensor Sentinel-1 and Sentinel2 (S1/S2) remote sensing data with environmental covariates (including climate, vegetation, topography, and soil properties). This integration is achieved via a random forest regression model on the Google Earth Engine (GEE) platform, thereby facilitating the generation of 90-meter resolution Ks maps in China's drylands. More than 5,000 lab-based Ks samples were employed to assess the contributions of synthetic aperture radar (SAR), optical, and environmental variables. The results demonstrated that high-resolution remote sensing data significantly improve the accuracy of Ks prediction at both surface (0-10 cm) and subsurface (10-30 cm) layers. Specifically, the root mean square error (RMSE) of ln(Ks/(cm min-1)) ranged from 1.24 to 1.61, and the coefficient of determination (R2) from 0.61 to 0.79. The generated 90-m Ks map outperformed the existing global and regional datasets in terms of both spatial detail and statistical accuracy. Validation using Taylor diagrams and probability density functions confirmed a closer match with the field data distributions, with the standard deviation reduced by 13.61%-22.81% compared to benchmarks. In particular, our findings elucidated the finescale heterogeneity influenced by soil texture and topography-such as the high-Ks zones in the Loess Plateau and Taklamakan Desert, which were not clearly visible in coarser products, were successfully resolved. This approach bridges the gap between coarse-scale PTFs and the requirement for high-resolution hydrological inputs. Additionally, it provides a scalable solution for arid and semi-arid regions worldwide.