Abstract
The Operational Linescan System (OLS) carried by the National Defense Meteorological Satellite Program (DMSP) can capture the weak visible radiation emitted from earth at night and produce a series of annual cloudless nighttime light (NTL) images, effectively supporting multi-scale, long-term human activities and urbanization process research. However, the interannual instability and sensor bias of NTL time series products greatly limit further studies of lighting data in time series with OLS. Several calibration models for OLS have been proposed to implement interannual corrections to improve the continuity and consistency of time series NTL products; however, due to the subjective factors intervention and insufficient automation in the calibration process, the interannual correction study of NTL time series images is still worth being developed further. Therefore, to avoid the involvement of subjective factors and to optimize the Pseudo-Invariant Features (PIF) identification, an interannual calibration model Pixel-based PIF (PBPIF) is proposed, which identifies PIF by pixel fluctuation characteristics. Results show that a PBPIF-based model can reduce subjective interference and improve the degree of automation during the NTL interannual calibration process. The calibration performance evaluation based on Total Sum of Lights (TSOL) and Sum of the Normalized Difference Index (SNDI) shows that compared to the traditional PIF-based (tPIF-based) and Ridgeline Sampling Regression based (RSR-based) models, the PBPIF-based one achieves better performance in reducing NTL interannual turbulence and minimizing the deviation between sensors. In addition, based on the corrected NTL time series products, pixel-level linear regression analysis is implemented to maximize the potential of the NTL resolution to produce global Light Intensity Change Coefficient (LICC). The results of global LICC can be widely applied to the detailed study of the characteristics of economic development and urbanization.
Highlights
Nighttime light (NTL) images effectively depict the distribution of artificial light on the earth’s surface, and have become an important indicator of the intensity of urban social and economic activities, and has been widely used in urbanization research [1,2,3,4,5,6,7]
Elvidge et al [11] and Miller et al [12] have demonstrated the superior performance of the new generation of NPP/VIIRS Day/Night Band (DNB) data in terms of light range, radiation calibration and resolution, especially compared to Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) NTL data; the value of long-term historical DMSP/OLS NTL archives should not be underestimated, as it is an irreplaceable reference for recording global human activities, revealing the global economy and exploring urban development patterns in past decades [13]
In NTL time series datasets, the Total Sum of Lights (TSOL) recorded by different sensors in the same year is biased, due to the slightly different working conditions and performance between OLS sensors mounted on different satellites
Summary
Nighttime light (NTL) images effectively depict the distribution of artificial light on the earth’s surface, and have become an important indicator of the intensity of urban social and economic activities, and has been widely used in urbanization research [1,2,3,4,5,6,7]. DMSP/OLS NTL datasets have been widely applied in urbanization and economic development research around the world, including urban impervious expansion [14,15,16], urban development models [17,18,19] or population and Gross Domestic Product (GDP) estimation [20,21,22,23,24]. Two main defects and limitations can be recognized in DMSP/OLS NTL time series datasets: the interannual instability of the same sensors and the deviations between different sensors in the same year. In NTL time series datasets, the TSOL recorded by different sensors in the same year is biased, due to the slightly different working conditions and performance between OLS sensors mounted on different satellites. The systematic error correction (inter-annual correction) for comparative analysis of historical NTL time series datasets must be implemented
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