Previous investigations of urban heat islands (UHI) are primarily focused either on the canopy heat island intensity (aUHII) derived from weather stations, or on the surface urban heat island intensity (sUHII) derived from satellite instruments. Research of the relationship between sUHII and aUHII (the sUHII‐aUHII relationship) is limited and this study attempts to further progress this possibility by examining the night‐time sUHII‐aUHII relationship for three factors: season, wind speed (WS), and basic land‐use categories modified from local climate zones (urban/suburban), in Birmingham, United Kingdom. Using high‐resolution datasets of canopy air temperature from Birmingham Urban Climate Laboratory and land surface temperature from the MODIS instrument aboard the Terra and Aqua satellites, with a unique methodology of regression analysis, confidence ellipse analysis of covariance (ANCOVA), and 2‐D Kolmogorov–Smirnov (K‐S) tests, statistical evidence is provided to present the varying patterns and magnitudes between sUHII and aUHII. The significance of the impact of the three considered factors is clearly supported by the statistical tests. The results indicate that satellite data can be used to infer aUHII with a higher confidence for low WS conditions. Results also demonstrate better confidence in the approach for summer and spring seasons, and for more urbanized sites. Indeed, the analysis potentially indicates that wind advection is a key factor for the investigation of the sUHII‐aUHII relationship. Overall, the methods used here are transferable to other cities and/or can be used to guide further research to explore the sUHII‐aUHII relationship under other environmental conditions. The linear sUHII‐aUHII relationship significantly varies with respect to the three moderator variables: wind speed (WS), season and site characteristics. Results indicate that satellite data can be used to infer aUHII with a higher confidence for low WS conditions. Results also demonstrate better confidence in the approach for summer and spring seasons and for more urbanized sites. For the WS category, the decrease of the slope of linear regression model (LRM) with increasing WS is explained by the same decreasing trend of the value of covariance between sUHII and aUHII, Cov(sUHII, aUHII); subsequently, the decreasing Cov(sUHII, aUHII) with WS is partially attributed to wind advection which causes different shifts of the spatial pattern of sUHII and aUHII. The larger slopes in summer and spring are partly explained by the lower WS conditions during these two seasons; however, further investigations of other causative factors are needed. The quadrant analysis applied to two land‐use groups (urban vs. suburban) has yielded the evidence to support the argument that wind advection may be responsible for the lower correlation between sUHII and aUHII for the suburban group. Therefore wind advection may obscure the interpretation of Ts for the purpose of representing aUHII by sUHII, and caution should be taken particularly for suburban locations.