Soil moisture estimates are crucial for hydrologic modeling and agricultural decision-support efforts. These measurements are also pivotal for long-term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been the past decade during which ground-based soil moisture sensory resources have become sufficient to tackle these important challenges. Despite this progress, random and systematic errors remain in ground-based soil moisture observations. Such errors must be quantified (and/or adequately minimized) before such observations can be used with full confidence. In response, this paper calibrates and analyzes US Climate Reference Network (USCRN) profile estimates at each of three sensors collocated at each USCRN location. With each USCRN location consisting of three independent, Hydraprobe measurements, triple collocation analysis of these sensory triads reveals the random error associated with this particular sensing technology in each individual location. This allows quantification of the accuracy of these individual profiles, the random errors associated with these measurements in different geographic locations, and offers the potential for more adept quality control procedures in near real time. Averaged over USCRN gauge locations nationally, this random error is determined to be approximately 0.012 m3/m3.