<p>Soil moisture near the surface and subsurface is significant for estimating crop water demand in rainfed areas. Modeling approaches have proven its efficiency in dividing the rainfall into surface and subsurface components in a given system. Calibrating the model parameters is important for simulating the actual scenario which assesses the performance of the model and extends its application. Variable Infiltration Capacity (VIC) model is used in this study to estimate the soil moisture by parameterizing the crop and soil properties at a 5 Km spatial resolution with homogenous crop and soil. The measured volumetric soil moisture is taken from the probe installed at G. B. Pant University of Agriculture and Technology, Pantnagar, India for calibration at different end states. The site-specific information is forced into the model, and the model run is made for both monsoon and non-monsoon season. The calibrated soil moisture is compared against the simulated for each iteration. The statistical performance of the model varies between 0.81 and 0.89 for correlation coefficient (R), 0.1 to 0.70 for Nash Sutcliffe Efficiency (NSE) and 0.77 to 0.80 for Kling Gupta Efficiency (KGE). The performance of model estimates in terms of P-Bias varies from 9.02 to 9.38 in the respective layers. This study concludes that the definition of root fraction, Leaf Area Index and local meteorological conditions plays a crucial role and are found to be the sensitive components for estimating water balance components in modeling framework. The crop and soil specific calibration enhances the further understanding of hydrological processes.</p>