Mapping the response of volumetric soil water content to an intense rainfall event at the field scale using GPR

•We determined parameters and empirical models for GPR inversion.•Spatial distributions of VSWC at different depths before and after rainfall were mapped.•Infiltrated rainwater was mainly stored in 20–40 cm soil after rainfall.•We discussed the effects of land uses and rainfall on VSWC and radar detection accuracy. Ground-penetrating radar (GPR) is a convenient tool for volumetric soil water content (VSWC) estimation in hydrological and agricultural studies. Although case studies have been widely carried out, little attention has been paid to subsoil moisture estimates. In this research, we investigated three-dimensional soil moisture variation down to a depth of 1 m and the effect of rainfall events on spatial soil moisture dynamics. GPR surveying lines were conducted both before and after a heavy rainfall event to map the VSWC. Soil sampling and time domain reflectometry (TDR) probe data at different depths (20, 40, 60, 80, and 100 cm) were acquired. Our results demonstrated that there was a significant correlation between the dielectric constants and VSWCs at all depths. The established relationships for the different depth ranges had a low VSWC discrepancy when the dielectric constants ranged from 10 to 15. The effective range of each variogram was larger than 20 m, except for that of the 0–100 cm VSWC map after rainfall. In addition, the validation diagrams using corrected TDR values demonstrated relatively reliable VSWC maps. Approximately 89% of the variation in VSWC could be explained by the dielectric constants in the depth range of 0–40 cm, and VSWC predictions at this soil depth outperformed those at other depth ranges, with an overall RMSE of 0.027 m3 m−3 and R2 of 0.725. Furthermore, we also monitored the effect of precipitation on the accuracy of the VSWC prediction on shallow surfaces. Our study shows that three-dimensional soil moisture dynamics can be accurately estimated at the field scale by integrating GPR interpretation and spatial extrapolation methods.