Soil water is essential for maintaining global food security and for understanding hydrological, meteorological, and ecosystem processes under climate change. Successful monitoring and forecasting of soil water dynamics at high spatio-temporal resolutions globally are hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil water monitoring schemes via station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via data fusion of remote sensing (Sentinel-1 and Soil Moisture Active and Passive Mission - SMAP) and land surface parameters (e.g. soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed moderately well across the globe under cropland, grassland, savanna, barren, and forest soils (R = 0.53, RMSE = 0.08 m m). SSM was retrieved and mapped at 100 m every 6-12 days in selected irrigated cropland and rainfed grassland in the OZNET network, Australia. It was concluded that the high-resolution SSM maps can be used to monitor soil water content at the field scale for irrigation management. The SSM model is an additive and adaptable model, which can be further improved by including soil moisture network measurements at the field scale. Further research is required to improve the temporal resolution of the model and map soil water content within the root zone.