Land Use Regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO<sub>2</sub>) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed effects LUR model is developed to model daily surface NO<sub>2</sub> concentrations over the Hong Kong SAR during 2005-2015. In-situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in-situ data shows that the mixed effect LUR model using OMI data has a high predictive power (adj. <i>R</i><sup>2</sup> = 0.84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. <i>R</i><sup>2</sup> = 0.11). Time series analysis shows no statistically significant trend in NO<sub>2</sub> concentrations during 2005-2015, despite a reported decline in NO<sub><i>x</i></sub> emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO<sub>2</sub> for use in exposure studies.