||Urbanization process involving increased population size, spatially extended land cover and intensified economic activity plays a substantial role in anthropogenic environment changes. Remotely sensed nighttime lights datasets derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) provide a consistent measure for characterizing trends in urban sprawl over time (Sutton, 2003). The utility of DMSP/OLS imagery for monitoring dynamics in human settlement and economic activity at regional to global scales has been widely verified in previous studies through statistical correlations between nighttime light brightness and demographic and economic variables ( and ). The quantitative relationship between long-term nighttime light signals and urbanization variables, required for extensive application of DMSP/OLS data for estimating and projecting the trajectory of urban development, however, are not well addressed for individual cities at a local scale. We here present analysis results concerning quantitative responses of stable nighttime lights derived from time series of DMSP/OLS imagery to changes in urbanization variables during 1994â2009 for more than 200 prefectural-level cities and municipalities in China. To identify the best-fitting model for nighttime lights-based measurement of urbanization processes with different development patterns, we comparatively use three regression models: linear, power-law and exponential functions to quantify the long-term relationships between nighttime weighted light area and four urbanization variables: population, gross domestic product (GDP), built-up area and electric power consumption. Our results suggest that nighttime light brightness could be an explanatory indicator for estimating urbanization dynamics at the city level. Various quantitative relationships between urban nighttime lights and urbanization variables may indicate diverse responses of DMSP/OLS nighttime light signals to anthropogenic dynamics in urbanization process in terms of demographic and economic variables. At the city level, growth in weighted lit area may take either a linear, concave (exponential) or convex (power law) form responsive to expanding human population and economic activities during urbanization. Therefore, in practice, quantitative models for using DMSP/OLS data to estimate urbanization dynamics should vary with different patterns of urban development, particularly for cities experiencing rapid urban growth at a local scale.