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
