This protocol is composed of five steps for downscaling satellite soil moisture estimates using digital terrain parameters derived from a digital elevation models. We provide an alternative approach to predict soil moisture spatial patterns at higher spatial resolution (compared with current satellite soil moisture estimates) across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information. This approach has been tested for this study across the conterminous United States on an annual basis (1991-2016). We first derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. We validate the models using cross-validation strategies and independent validation. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations with field soil moisture observations from the North American Soil Moisture Database (n=668 locations with available data between 1991-2013; 0-5cm depth) than the original product. We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.