Soil Clustering Using Geophysical and Remote Sensing Data: Implications for Water Management Zones

Traditional soil management relies on “whole-field” averages, which leads to resource waste and environmental degradation under anthropogenic pressures. While combining electromagnetic induction (EMI) and remote sensing is known for digital soil mapping, current approaches lack a unified, automated framework to handle complex multi-source data dependencies for local-scale precision irrigation. To overcome this limitation, this study introduces a novel integrated methodology that couples high-resolution geophysical datasets and remote/proximal sensing through an automated machine learning workflow, capturing dynamic soil–human interaction boundaries more precisely than traditional empirical overlays. The general methodology was tested in a vineyard plot within the Torre Guaceto Natural Reserve (Southern Italy). Spatial datasets from EMI and remote sensing were integrated. Crucially, the K-means clustering algorithm was deployed early in the workflow to optimize the fused datasets and classify the plot into homogeneous zone clusters. The machine learning approach successfully identified two distinct main soil clusters. The spatial boundaries of these zones were rigorously validated using in situ soil moisture data from capacitance sensors, showing a statistically significant variance in volumetric water content between the two zones. This study demonstrates that integrated machine learning workflows can accurately delineate precision agricultural zones without relying on high-cost exhaustive sampling. It is recommended that farmers and managers within sensitive nature reserves adopt this cluster-based Variable Rate Application (VRA) for water and fertilizers to optimize resource efficiency and prevent nutrient leaching into underlying aquifers.