Soil moisture or soil water content is an important variable for Meteorology, Agronomy, Hydrology and Soil Science. In Argentina, measurements of soil moisture are both spatially and temporally sparsely distributed. There are different methodologies of measuring soil moisture content like the gravimetric, neutron and dielectric methods, among others. During the growing season 2012-2013, soil moisture measurements were performed in a soybean crop field. The aim of this paper is to calibrate observed capacitive data with gravimetric and neutron methods as well as to build a high temporal resolution soil moisture database. Linear functions were used to calibrate data for each depth of measurement with the exception of the two deepest levels where retrieved functions have a poor data fitting. The quality of the observed data and the calibration functions have direct impact on the capacitive sensor errors. Gravimetric, neutron and capacitive soil moisture measurements were interpolated with the Kriging method to construct the high resolution database. Variability of soil water content was observed at different soil depths due to precipitation and plant water uptake.