Site-specific crop management utilizes site-specific management units (SSMUs) to apply inputs when, where, and in the amount needed to increase food productivity, optimize resource utilization, increase profitability, and reduce detrimental environmental impacts. It is the objective of this study to demonstrate the delineation of SSMUs using geospatial apparent soil electrical conductivity (ECa) and bare-soil reflectance measurements. The study site was a 21-ha field at the southern margin of the Venice Lagoon, Italy, which is known to have considerable spatial variability of soil properties influencing crop yield. Maize (Zea mais L.) yield maps from 2010 and 2011 showed high spatial heterogeneity primarily due to variation in soil-related factors. Approximately 53% of the spatial variation in maize yield was successfully modeled according to the variability of four soil properties: salinity, texture, organic carbon content, and bulk density. The spatial variability of these soil properties was characterized by the combined use of intensive geospatial ECa measurements and bare-soil normalized difference vegetation index (NDVI) survey data. On the basis of the relationships with these soil properties, ECa and NDVI were used to divide the field into five SSMUs using fuzzy c-means clustering: one homogeneous with optimal maize yield, one unit affected by high soil salinity, one characterized by very coarse texture (i.e., sandy paleochannels), and two zones with both soil salinity and high organic carbon content. Yield monitoring maps provide valuable spatial information, but do not provide reasons for the variation in yield. However, even in cases where measurements like ECa and bare-soil NDVI are not directly correlated to maize yield, their combined use can help classify the soil according to its fertility. The identification of areas where soil properties are fairly homogeneous can help managing diverse soil-related issues optimizing resource use, lowering costs, and increasing soil quality.