A study validates a Cross-Correlation Function method to identify optimal temporal and depth lags between meteorological variables and soil moisture. Using satellite and meteorological data across seven agricultural plots in southeastern Spain, deep learning models achieved significant improvements: a per-pixel CNN reached R² = 0.877, while a CNN-LSTM hybrid achieved the highest overall performance with R² = 0.930. Subsurface depth information and meteorological features substantially enhanced estimation accuracy.