NPP/visible infrared image radiometer suite low-light image fusion algorithm for city lights in mid-eastern China

Yu Yue, Hu Xiuqing*, Min Min, Xu Tingfa, He Yuqing, Chen Lin, Xu Ronghan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

As part of the next-generation American environmental meteorological satellites, the NPP/Joint Polar Satellite System (JPSS) has introduced the Visible Infrared Image Radiometer Suite (VIIRS) Day and Night Band (DNB) low-light day/night imaging band. DNB employs the ability of defense meteorological satellite program (DMSP)/Operational Linescan System (OLS) to collect global low-light imaging data, which has resulted in a considerably optimized performance. The nighttime city lights can be detected using the VIIRS DNB data. Because of the urgent requirement for city lights in the rapidly developing regions that arc located in mid-eastern China, this study comprehensively considers the influence of various important factors, including clouds, moonlight, sunlight, lightning, and fire, while identifying and rejecting low-quality data and non-light characteristic data in addition to adopting VIIRS DNB multi-day nighttime data fusion. The average of multi-day non-moonlit effective radiance data is used to generate monthly nighttime city lights in mid-eastern China. The results that arc obtained by analyzing the radiance value of the product arc comparable with those obtained by analyzing the National Oceanic and Atmospheric Administration (NOAA) city lights business product. The city lights fusion product algorithm, which is developed in this study, provides the technical foundation to develop the back-end generative application and for the inversion of the remaining low-light remote-sensing nighttime products.

Original languageEnglish
Article number102801
JournalLaser and Optoelectronics Progress
Volume55
Issue number10
DOIs
Publication statusPublished - 2021

Keywords

  • City lights
  • Low-light
  • Multi-temporal fusion
  • Radiance
  • Remote sensing

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