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Volume 13
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10.3390/rs13245026
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Article

Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net

by
Dmitry Nechaev
1,
Mikhail Zhizhin
2,3,*,
Alexey Poyda
1,4,
Tilottama Ghosh
2,
Feng-Chi Hsu
2 and
Christopher Elvidge
2
1
Moscow Institute of Physics and Technology, 117303 Moscow, Russia
2
Earth Observation Group, Payne Institute, Colorado School of Mines, Golden, CO 80401, USA
3
Space Research Institute, Russian Academy of Sciences, 117997 Moscow, Russia
4
NRC “Kurchatov Institute”, 123182 Moscow, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(24), 5026; https://doi.org/10.3390/rs13245026
Submission received: 7 September 2021 / Revised: 3 November 2021 / Accepted: 6 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Artificial Intelligence in Nighttime Remote Sensing)
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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 nighttime lights; cross-sensor calibration; DMSP/OLS; SNPP/VIIRS; convolutional neural network

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MDPI and ACS Style

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

AMA Style

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 Sensing. 2021; 13(24):5026. https://doi.org/10.3390/rs13245026

Chicago/Turabian Style

Nechaev, Dmitry, Mikhail Zhizhin, Alexey Poyda, Tilottama Ghosh, Feng-Chi Hsu, and Christopher Elvidge. 2021. "Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net" Remote Sensing 13, no. 24: 5026. https://doi.org/10.3390/rs13245026

APA Style

Nechaev, D., Zhizhin, M., Poyda, A., Ghosh, T., Hsu, F.-C., & Elvidge, C. (2021). Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net. Remote Sensing, 13(24), 5026. https://doi.org/10.3390/rs13245026

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