Land use regression (LUR) modelling has increasingly been applied to model fine scale spatial variation of outdoor air pollutants including nitrogen dioxide (NO₂). Satellite observations of tropospheric NO₂ improved LUR model in very large study areas, including Canada, United States and Australia. The aim of our study was to assess the value of satellite observations of NO₂ in modelling the spatial variation of annual average NO₂ concentrations in a small densely populated country. We used surface level annual average NO₂ concentration and geographic information system data from 144 monitoring sites spread over the Netherlands: 26 regional background, 78 urban background and 40 traffic sites for developing land use regression models. For the 144 monitoring sites we obtained the annual average tropospheric NO₂ concentration for 2007 from the Ozone Monitoring Instrument (OMI) satellite sensor. These OMI data reflect a spatial scale of about 10×10km. We calculated the correlation between satellite and surface level NO₂ concentrations for all sites and for background sites only. We next evaluated whether adding satellite observations improved land use regression models.Annual average satellite observations of tropospheric NO₂ correlated well spatially with annual average urban plus regional background (R=0.74, n=104 sites) and especially regional background NO₂ concentrations (R=0.88, n=26). The correlation was moderate for all sites, including traffic locations (R=0.51, n=144). A LUR model including satellite NO₂ observations performed better (overall R²=0.84) than LUR models including geographical coordinates or indicator variables (overall R² 65-74%) in modeling concentrations at the 104 background sites across the Netherlands.Satellite NO₂ observations agreed well with measured surface concentrations at background locations and improved land use regression models, even in a small densely populated country.