Modeling and prediction of soil hydrologic processes require the identification of soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for the combination of spatially- and temporally-extensive soil moisture data obtained in the Shale Hills Critical Zone Observatory (CZO) from 2004 to 2010. The soil moisture data sets were decomposed into spatial Empirical Orthogonal Function (EOF) patterns, and their relationship with various geophysical parameters was examined to determine the dominant factors contributing to the profiled soil moisture variability. The EOF analyses indicated that one or two EOFs of soil moisture could explain 76-89% of data variation. The primary EOF pattern had high values clustered in the valley region, and conversely low values located in the sloped hills. We suggest a novel approach to integrate the spatially-extensive manually measured datasets with the temporally-extensive automated monitored datasets based on the EOF analyses. Given the data accessibility, the current data merging framework has provided the methodology for the coupling of the mapped and monitored soil moisture datasets, as well as the conceptual coupling of slow and fast pedologic and hydrologic functions. This successful coupling implies that a combination of different extensive moisture data has provided interesting insights into our understanding of hydrological processes at multiple scales.