Short-scale spatial variability of soil properties need to be identified for the proper management of soil resources for crop production. Proximally sensing of soil apparent electrical conductivity (ECa) and the application of inversion technique are highly potential approaches to predict the spatial variability of soil properties. This study was carried out to investigate the applicability of ECa data together with inversion technique to predict the spatial variability of soil variability in Calcic Red Latosols. DUALEM-1S sensor was used to perform the ECa survey in an agricultural land (3.2 ha) situated in Allaveddy in the Jaffna district. The acquired ECa data were used to predict ECa at 20 cm depth increments down to 80 cm soil depth. Exploratory data analyses and then local kriging procedure were applied separately for original and inverted ECa data to construct continuous maps. Soil samples were taken from six sample points (at 20 cm depth intervals upto 80 cm from each sample point) using the purposive sampling scheme. Soil samples were analyzed for soil texture, organic matter, electrical conductivity (EC) and pH. Proximally sensed ECaPRP (CV = 45.4%) and ECaHCP (CV = 73.5%) and the depth profiles of different soil properties showed a high vertical and horizontal spatial variability of soil in the site. High correlations were shown between EC (measured at different depths) and both ECaPRP (r >0.60) and ECaHCP (r >0.60) at different depths. However, ECa did not show strong correlations with other soil properties. The high correlations (r > 0.76) between depth specific inverted ECaPRP and ECaHCP measurements and measured EC of respective depths indicated that these ECa data layers can be used to map the soil salinity development in different soil layers. This study revealed a strong short-scale spatial variability of soil properties in the selected Calcic Red Latosol and proximal soil sensing using the DUALEM-1S sensor is a highly potential tool for producing three dimensional maps of the soil EC.