Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes. Traditional research often relies on sensors or remote sensing data to examine the hydrological properties of vegetation zones, yet these methods are limited by either measurement sparsity or spatial inaccuracy. Therefore, this paper is the first to propose a data-driven approach that incorporates high-temporal-resolution electrical resistivity tomography (ERT) to quantify soil hydrological response. Time-lapse ERT is deployed on a vegetated slope site in Foshan, China, during a discontinuous rainfall induced by Typhoon Haikui. A total of 97 ERT measurements were collected with an average time interval of 2.7 hours. The Gaussian Mixture Model (GMM) is applied to quantify the level of response and objectively classify impact zones based on features extracted directly from the ERT data. The resistivity-moisture content correlation is established based on on-site sensor data to characterize infiltration and evapotranspiration across wet-dry conditions. The findings are compared with the Normalized Difference Vegetation Index (NDVI), a common indicator for vegetation quantification, to reveal potential spatial errors in remote sensing data. In addition, this study provides discussions on the potential applications and future directions. This paper showcases significant spatio-temporal advantages over existing studies, providing a more detailed and accurate characterization of superficial soil hydrological response.