In-situ soil moisture measurements play a key role for a variety of large-scale applications. A deep understanding of their quality, especially in terms of spatial representativeness, is crucial for reliably using them as reference data. This study assesses random errors in the coarse-scale representation of in-situ soil moisture measurements from more than 1400 globally distributed stations in the International Soil Moisture Network (ISMN) using the triple collocation method. The method was applied on the original measurements as well as on soil moisture anomalies. Error estimates were summarized for different networks, depths, and measurement principles and furthermore related to the respective climate class, soil type, average soil moisture condition, and soil moisture variability to find possible relationships between measurement errors and local properties. The average network error varies from about 0.02 to 0.06 m(hoch)3m(hoch)-3 with generally increasing error variability with increasing average error. Trends of (i) decreasing errors with increasing measurement depth and of (ii) increasing errors with increasing average soil moisture conditions and soil moisture variability were found for most networks and sensor types. The errors when looking into anomalies are in general lower than for absolute values. No statistically reliable trends for climate- and soil texture classes were found. The results highlight the necessity of developing a comprehensive quality control process for in-situ measurements to reliably exploit existing data sets and to select representative sites and sensors most appropriate for the requirements of a particular larger-scale application.