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The Defense Meteorological Satellite Program-Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) products have been widely employed for studying human activities and urbanization. However, due to the lack of on-board calibration of the OLS sensors, narrow radiation range, and different satellite platforms, the NSL products suffer from several limitations such as saturation effect and inconsistency problem. To solve these issues, this study proposed a spatio-temporal adaptive pseudo-invariant pixel (STAPIP) scheme for NSL time-series consistency calibration based on eight-year DMSP-OLS radiance calibrated (RC) products. The unsaturated pixels were calibrated with the temporal adaptive pseudo-invariant pixel (TAPIP) method, while the saturated pixels were calibrated with the spatial adaptive PIP (SAPIP) method. To verify the proposed method's effectiveness, we applied it in China and compared its results with the results of several existing NSL calibration methods. Then we further analyzed the correlations between the calibrated NSL time-series images and several important socio-economic indicators, such as population, gross domestic product (GDP), and electricity consumption (EC). Results showed that the NSL time-series images calibrated by the STAPIP method have the best consistency and highest correlation with socio-economic indicators at both provincial and city levels. Thus, this study can provide an accurate and stable NSL time-series product for research on human activities and the urbanization process.
Yang et al. (Sat,) studied this question.