A consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data

Area of Science:

• Earth Observation
• Remote Sensing
• Geospatial Analysis

Background:

• Nighttime light (NTL) remote sensing provides global data for urbanization and disaster assessment.
• Defense Meteorological Satellite Program's Operational Line Scan (DMSP-OLS) data (1992-2013) are valuable but suffer from inconsistencies, saturation, and blooming.
• These limitations hinder the full utilization of long time series NTL data.

Purpose of the Study:

• To improve the quality of DMSP-OLS NTL data.
• To generate a consistent and corrected NTL dataset for enhanced applications.
• To address interannual inconsistency, saturation, and blooming issues in existing NTL datasets.

Main Methods:

• Utilized a power function model with pseudo-invariant features.
• Applied a saturation correction method using regression models and radiance-calibrated data (SARMRC).
• Employed a self-adjusting model (SEAM) for data improvement.

Main Results:

• Developed the Consistent and Corrected Nighttime Light (CCNL) dataset (1992-2013).
• CCNL data demonstrate improved interannual consistency.
• Enhanced spatial details of urban centers and reduced light blooming effects.

Conclusions:

• The CCNL dataset significantly improves upon DMSP-OLS data quality.
• This improved dataset unlocks greater potential for long time series NTL data applications.
• CCNL facilitates more accurate urbanization monitoring and socio-economic estimations.