Defense Meteorological Satellite Program-Operational Line scan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) night-time light (NTL) datasets portraying nocturnal lit pixels have been widely applied to effectively monitor anthropogenic activities, temporal variation in urbanization, and assessment of the dynamics of socio-economic activities and development. Among the various challenges in conventional NTL remote sensing studies, one has been the development of a consistent and long-term NTL time series dataset, indispensable for extended duration analysis. Lack of onboard-calibration, low spatial and radiometric resolution causing saturation for dense urban built up and blooming effect exaggerating the urban extent; pose challenges in the efficient use of DMSP-OLS (2.7 km) data for urban development studies despite its availability since 1992. Therefore, this study attempts to generate high resolution enhanced DMSP-OLS dataset which could generate long-term and consistent NTL time series (1992-2021), intercalibrating with NPP-VIIRS (500 m) products considering invariant pixels. Considering NPP-VIIRS data as base, enhanced DMSP-OLS data are simulated for the period 2012-2013 (common to both retrieved datasets). Calibration of remaining DMSP-OLS datasets is performed utilizing the simulated products (for 2012, 2013) and taking advantage of neighborhood layers. Consequently, a consistent and more accurate NPP-VIIRS like NTL time series is achieved for the entire period overcoming the issue of saturation and blooming effects for DMSP-OLS products. The developed NTL time series is then used to study the urbanization patterns for major Indian metropolitan cities of Delhi, Mumbai, Chennai, and Kolkata. A significant increase in urbanization has been observed with decadal growth rate ranging from 20 to about 40 percent in the 30-year study period, depicting maximum growth proportion during the second decade, for majority of the cities. Further, adopting machine learning approach, Landsat 5 and 8 images have been classified to extract urban extent for the designated period and validate the intensity of urban development derived through NTL products. Keywords: Night-time lights, DMSP-OLS, NPP-VIIRS, urban growth