Many findings suggested that it’s possible to improve the accuracy of delineating site-specific management zones (SSMZs) through a combination of proximal data with remote sensing imagery. The objective of this study is to assess the feasibility of delineating SSMZs with a wide range of ancillary data (proximal survey and multi-spectral data). The study area is a 22.1acre located 10 miles north of Fort Collins, CO and is known for having a high spatial and temporal variability of soil properties. Combination of apparent electrical conductivity (ECa), elevation, analyzed soil parameters, bare soil imagery, and vegetation indices (NDVI, SAVI, GNDVI, and OSAVI) data over the growing period used to delineate SSMZs. Principal component analysis on data were applied to reduce the dimensionality, summarize high variability, and select the parameters with high variability sources. Site-specific management zones were clustered with ArcGIS pro and Management Zone Analyst (MZA) software with the fuzzy c-means clustering method based on the parameters that represented the highest variability. Three management zones are identified as the optimum number of zones based on the fuzziness performance index (FPI) and the normalized classification entropy index (NCE). The analysis of variance of soil properties within the management zone proved that there is variability among the three management zones and validate the feasibility of using a combination of proximal and multi-spectral data to delineate SSMZs, it’s recommended to have additional studies over multiple locations and yield as a ground truth data to further validate the feasibility of ancillary data for delineating SSMZs.