Soil apparent electrical conductivity (ECa) acquired by proximal sensor EM38, normalized difference vegetation (NDVI) extracted from high resolution remote sensing images and backscattering coefficient obtained from radar images were selected as the source data for farm field soil management zoning with the clustering method of the coastal saline soil for precision agriculture. So, based on spatial variability analysis of the data and the law for spatial variation of soil salinity in the area, zoning was done using the unsupervised classification method—fuzzy k-means cluster algorithm. Results show that the optimal number of zones was 3. In light of the characteristics of each zone, corresponding management practices should be adopted to ameliorate the soil and implement precision farmland management. The zoning in useful not only to guiding soil sampling, but also to recommendation of variable input and precision fertilization, and moreover, provides scientific basis for decision making in large-scale soil management.