Soil thermal conductivity (STC) is vital for environmental and engineering modeling, yet traditional unfrozen STC estimates often perform poorly under field conditions. This study develops an enhanced Johansen–Tarnawski model incorporating vegetation parameters (JT-V) and applies geospatial data for regional simulation. Residuals from mechanistic predictions were analyzed using Geodetector and Random Forest, revealing strong vegetation-type effects. Validation with 88 samples from 18 sites across five vegetation types showed the JT-V model significantly improved accuracy: R2 rose from 0.426 to 0.716, and RMSE decreased by 53%. The best performance occurred at the surface layer (RMSE = 0.074 W·m−1·K−1), with errors increasing with depth. Over 83% of sites achieved R2 > 0.7, and most linear regression slopes fell between 0.8 and 1.1. Applying JT-V to simulate thawing-season STC in Northeast China, it was found that lower values predominated in the Khingan Mountains and the Inner Mongolia Plateau, while higher values occurred across the Northeast Plain. Temporal dynamics exhibited three stages: stability (May–mid-July), rapid rise (mid-July–mid-August), and gradual decline (mid-August–September). The improved model advances regional land surface simulations and supports agricultural and engineering applications.
Liu et al. (Sat,) studied this question.