Extending the DMSP Nighttime Lights Time Series beyond 2013

Tilottama Ghosh,Mikhail Zhizhin,Alexey Poyda,Kimberly E Baugh,Feng-Chi Hsu,Christopher D Elvidge

doi:10.3390/rs13245004

Abstract

Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights.

Highlights

The nighttime lights of the world have emerged as the most reliable and globally consistent dataset for various scientific studies and applications
Since the Operational Linescan System (OLS) collected global nightly data, it was possible to filter out sunlit, moonlit, and cloudy data, visually ‘linescreen’ to exclude lights due to aurora and abrupt gain changes and make consistent products extending from 65 south to 75 north [18]
The first change is that instead of a professional analyst going through each orbit and selecting the lines to exclude lights due to aurora and solar glare, a Neural Network was trained based on lines selected for 3650 half orbits of 2018 [21,22]

Summary

Introduction

The nighttime lights of the world have emerged as the most reliable and globally consistent dataset for various scientific studies and applications. Since the OLS collected global nightly data, it was possible to filter out sunlit, moonlit, and cloudy data, visually ‘linescreen’ to exclude lights due to aurora and abrupt gain changes and make consistent products extending from 65 south to 75 north [18]. This series ended in 2013 due to orbital degradation of the DMSP. In the outlier-removed average composite, the background values change significantly from region to region. To gather samples of the background values, an analyst placed markers over areas in the outlier-removed composite, which visually appeared light-free. The Stable Lights mask was applied to the average visible band composite to create the final Stable Light composite product (Figure 12)

Methods

Results

Conclusion