This study quantitatively investigates the spatial variability of temperature dew-point depression (T-Td) thresholds associated with fog formation using road-based observations extracted from CCTV footage along the Yeongdong Expressway in Korea. The CCTV-derived fog occurrences were integrated with nearby meteorological observations and detailed topographic and land-cover information to examine how local environmental conditions influence the T-Td values observed during fog events. The analysis reveals that, even when fog was present, T-Td thresholds varied markedly among sites, reflecting differences in surrounding terrain, surface characteristics, and local microclimatic conditions. Locations exhibiting relatively small T-Td thresholds were predominantly found in low-lying, open, and gently sloping terrain near rivers, where nocturnal radiative cooling, moisture availability, and cold-air pooling are readily established. Under these conditions, condensation and fog formation can occur close to saturation, requiring only a small temperature dew point difference. In contrast, sites characterized by larger T-Td thresholds were generally associated with higher elevations, steeper slopes, and forest-dominated land cover. In such environments, enhanced turbulence, terrain-induced airflow, vegetation-mediated heat and moisture exchange, and increased air mixing tend to delay saturation or promote more complex pathways toward condensation. Consequently, fog may still form, but at comparatively larger T-Td values under more heterogeneous microclimatic conditions. These findings demonstrate that fog formation along road corridors cannot be adequately explained by atmospheric humidity conditions alone. Instead, the critical conditions for condensation and fog onset are strongly modulated by nonlinear interactions among topography, land cover, and local microclimate. The results underscore the importance of incorporating site-specific surface and terrain characteristics into road-fog prediction and risk assessment frameworks, providing empirical support for more localized and physically informed fog forecasting strategies.