Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor was used, along with high-accuracy topography data, to characterize soil variability from three agricultural fields in Ontario, Canada. Sentinel-2 data were used for measuring bare soil and historical vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python. In this NSA algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller relatively homogeneous areas. The algorithm was illustrated using field elevation, shallow and deep EC_a, soil pH, and several VIs.