摘要：Remote sensing of nighttime light can observe the artifcial lights at night on the planet’s surface . The Defense Meteorological Satellite Program’s Operational Line Scan (DMSP-OLS) data (1992–2013) provide planet-scale nighttime light data over a long-time span and have been widely used in areas such as urbanization monitoring, socio-economic parameters estimation, and disaster assessment . However, due to the lack of an on-board calibration system, sensor design defects, limited light detection range, and inadequate quantization levels, the applications of DMSP-OLS data are greatly limited by interannual inconsistency, saturation, and blooming problems . To address these issues, we used the power function model based on pseudo-invariant feature, the saturation correction method based on regression model and radiance-calibrated data (SARMRC), and the self-adjusting model (SEAM) to improve the quality of DMSP data, and generated a Consistent and Corrected Nighttime Light dataset (CCNL 1992–2013) . CCNL dataset shows good performance in interannual consistency, spatial details of urban centers, and light blooming, which is helpful to fully explore the application potentials of long time series nighttime light data .
