Research on urban poverty has moved on from an income–consumption perspective to a more informed and multi-dimensional approach in order to understand the multiple deprivations the poor face. Urban environmental quality, however, is one dimension that still gets little attention, particularly in data-poor environments. The research in this chapter proposes a remote-sensing-based approach for including environmental aspects in the analysis of urban deprivation, taking the city of Kalyan-Dombivli (India) as a case for study. An index of environmental quality was constructed on the basis of three indicators: thermal environment, distribution of greenery, and building density, derived from Landsat TM and ETM+, IRS Resourcesat and QuickBird images, respectively. Further, the impact of street orientation was analysed with the help of a digital surface model of the city that was created using Cartosat-1 stereo pair images. Finally, the spatial heterogeneity of the three constituents of the environmental quality index was explored at sub-ward level (to avoid data aggregation of rather heterogeneous wards). The overall index of environmental quality shows deprived areas to be worst off in terms of the indicators included in the index. Moreover, environmental quality varied across the city, both when exploring it at the pixel level as at the level of electoral ward.