Soil moisture signatures provide a promising solution to overcome the difficulty of evaluating soil moisture dynamics in hydrologic models. Soil moisture signatures are metrics that represent catchment dynamics extracted from time series of data and enable process-based model evaluations. To date, soil moisture signatures have been tested only under limited land-use types. In this study, we explore soil moisture signatures’ ability to discriminate different dynamics among contrasting land-uses. We applied a set of nine soil moisture signatures to datasets from six in-situ soil moisture networks worldwide. The dataset covers a range of land-use types, including forested and deforested areas, shallow groundwater areas, wetlands, housing areas, grazed areas, and cropland areas. These signatures characterize soil moisture dynamics at three temporal scales: event, seasonal, and time-series scales. Statistical and visual assessment of extracted signatures showed that (1) storm event-based signatures can distinguish different dynamics for most land-uses, (2) season-based signatures are useful to distinguish different dynamics for some types of land-uses (forested vs. deforested area, greenspace vs. housing area, and deep vs. shallow groundwater area), (3) timeseries-based signatures can distinguish different dynamics for some types of land-uses (forested vs. deforested area, deep vs. shallow groundwater area, non-wetland vs. wetland area, and ungrazed vs. grazed area). We compared signature-based process interpretations against literature knowledge: event-based and time series-based signatures were generally matched well with previous process understandings from literature, but season-based signatures did not. This study demonstrates the best practices of extracting soil moisture signatures under various land-use and climate environments and applying signatures for model evaluations.