Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data

Abstract

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.