This paper presents our approach to benchmarking the accuracy of DASSP's downscaled soil moisture estimates. Benchmarking requires the selection of appropriate reference soil moisture data sets and the development of relevant metrics for evaluating the similarity between the reference data set and DASSP’s modeled outputs. For our reference data, we considered a variety of sources, including global collections of in situ point measurements such as the International Soil Moisture Network (ISMN), infrequent but spatially-dense remotely-sensed measurements such as NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) campaigns, and high-resolution single-catchment in situ data sets such as the Western and Grayson 1998 Tarrawarra collection. We discuss the strengths and weaknesses of the different types of data sets for use in benchmarking DASSP’s downscaled soil moisture predictions. We then quantify the agreement of DASSP’s predictions with the reference data sets. We found that the single-catchment data sets were most useful for benchmarking, despite their small geographic size, as their reported data were more detailed and consistent and their biases were easier to address than the larger-scale heterogeneous databases that we considered.