Global vegetation greening is reshaping water and energy cycles, challenging land surface hydrological modeling. Satellite remote sensing provides dynamic observations of vegetation and radiation, offering a pathway to improve simulations. However, models often rely on static parameters, failing to capture critical transient biogeophysical feedbacks. This study quantifies the impact of integrating remote sensing data—leaf area index, fractional vegetation cover, albedo, and downward radiation—into the Variable Infiltration Capacity (VIC) model across China. We evaluated simulations against observed runoff from 50 stations, evapotranspiration (ET) from >40 flux sites, and satellite ET products. The dynamic data-driven VIC model accurately simulated runoff and ET. In ungauged basins, simple parameter transfer achieved Nash-Sutcliffe efficiency >0.6 for runoff. Using static vegetation parameters induced substantial biases: a national-scale ET underestimation of 5 % (20 mm yr^−1) and runoff overestimation of 14 % (29 mm yr^−1). In the rapidly greening basin (i.e., Pearl River Basin), dynamic vegetation data corrected ET and runoff biases by ~70 mm yr^−1. Remote sensing radiation data offered limited improvement, likely due to the model's inherent radiation estimation capability. This work provides conclusive evidence that dynamic remote sensing data, particularly vegetation parameters, are crucial for accurate large-scale hydrological simulation in changing environments, offering a practical framework for data-sparse regions.