This study investigates time lags between environmental changes, electrolyte formation, and atmospheric corrosion sensor responses under controlled multi-droplet wetting. A commercial corrosion and environmental sensor was combined with in-situ microscopy, and an Artificial Intelligence (AI)-based segmentation approach was applied to track droplet growth. A cross-correlation analysis identified and quantified time lags between Surface Relative Humidity (SRH), droplet radius, and sensor responses based on Interdigitated Electrodes (IDE) measuring conductance, galvanic corrosion, and free corrosion. This approach ultimately aids in understanding how environmental fluctuations affect the dynamic behaviour of the electrolyte layer and, in turn, influence atmospheric corrosion sensor responses.