remote sensing
Article
Cross-Sensor Nighttime Lights Image Calibration for
DMSP/OLS and SNPP/VIIRS with Residual U-Net
Dmitry Nechaev
1
, Mikhail Zhizhin
2,3,
* , Alexey Poyda
1,4
, Tilottama Ghosh
2
, Feng-Chi Hsu
2
and Christopher Elvidge
2
Citation: Nechaev, D.; Zhizhin, M.;
Poyda, A.; Ghosh, T.; Hsu, F.-C.;
Elvidge, C. Cross-Sensor Nighttime
Lights Image Calibration for
DMSP/OLS and SNPP/VIIRS with
Residual U-Net. Remote Sens. 2021, 13,
5026. https://doi.org/10.3390/
rs13245026
Academic Editors: Gang Chen and
Yasushi Yamaguchi
Received: 7 September 2021
Accepted: 6 December 2021
Published: 10 December 2021
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4.0/).
1
Moscow Institute of Physics and Technology, 117303 Moscow, Russia; nechaev.dv@phystech.edu (D.N.);
poyda_aa@nrcki.ru (A.P.)
2
Earth Observation Group, Payne Institute, Colorado School of Mines, Golden, CO 80401, USA;
tghosh@mines.edu (T.G.); fengchihsu@mines.edu (F.-C.H.); celvidge@mines.edu (C.E.)
3
Space Research Institute, Russian Academy of Sciences, 117997 Moscow, Russia
4
NRC “Kurchatov Institute”, 123182 Moscow, Russia
* Correspondence: mzhizhin@mines.edu
Abstract: Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of
economic growth, urbanization, stability of power grid, environmental light pollution, pandemics
and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan
System (OLS) onboard the satellites from the Defense Meteorology Satellite Program (DMSP) and
(2) Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP (SNPP) and NOAA-
20 satellites from the Joint Polar Satellite System (JPSS). However, the nighttime images acquired
by these two sensors are incompatible in spatial resolution and dynamic range. To address this
problem, we propose a method for the cross-sensor calibration with residual U-net convolutional
neural network (CNN). The CNN produces DMSP-like NTL composites from the VIIRS annual NTL
composites. The pixel radiances predicted from VIIRS are highly correlated with NTL observed
with OLS (0.96 < R2 < 0.99). The method can be used to extend long-term series of annual NTL
after the end of DMSP mission or to cross-calibrate same year NTL from different satellites to study
diurnal variations.
Keywords: nighttime lights; cross-sensor calibration; DMSP/OLS; SNPP/VIIRS; convolutional
neural network
1. Introduction
Defense Meteorology Satellite Program (DMSP) was launched in 1962 and since then
its satellites with Operational Line-scan System (OLS) serve as a source of valuable night-
time light (NTL) data of the Earth surface. Starting from 1992 the DMSP satellites broadcast
digital images, which were post-processed by the NOAA (currently Colorado School
of Mines) Earth Observation Group (EOG) into global annual average and background
removed NTL maps. With annual data stretch from 1992 to 2013, it makes DMSP Nighttime
Lights the longest data series available for nocturnal remote sensing on human activities [1].
In 2011 Suomi NPP (SNPP) satellite with Visible Infrared Imaging Radiometer Suite (VIIRS)
was launched. VIIRS instrument is also capable of detecting dim light sources at night.
Annual VIIRS maps of nighttime lights are published from 2013 to 2019 [2]. NTL maps
made with DMSP (DNL) or VIIRS (VNL) are widely utilized in research of human activity,
economy and ecology [3].
Despite the value of the long-term archives from DMSP and VIIRS, their potential
is not fully utilized. The main reason is that there is a significant difference between
the OLS and VIIRS sensors (Table 1). VIIRS is significantly superior to OLS in terms of
spatial, temporal, and radiometric resolution [4]. Due to the lack of calibration, DMSP data
have no radiometric measurement units whilst VIIRS output is measured in nW/cm
2
/sr.
Remote Sens. 2021, 13, 5026. https://doi.org/10.3390/rs13245026 https://www.mdpi.com/journal/remotesensing