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Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data

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  • Jinpei Ou
  • Xiaoping Liu
  • Xia Li
  • Meifang Li
  • Wenkai Li

Abstract

Recently, the stable light products and radiance calibrated products from Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) have been useful for mapping global fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution. However, few studies on this subject were conducted with the new-generation nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite, which has a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. Therefore, this study performed the first evaluation of the potential of NPP-VIIRS data in estimating the spatial distributions of global CO2 emissions (excluding power plant emissions). Through a disaggregating model, three global emission maps were then derived from population counts and three different types of nighttime lights data (NPP-VIIRS, the stable light data and radiance calibrated data of DMSP-OLS) for a comparative analysis. The results compared with the reference data of land cover in Beijing, Shanghai and Guangzhou show that the emission areas of map from NPP-VIIRS data have higher spatial consistency of the artificial surfaces and exhibit a more reasonable distribution of CO2 emission than those of other two maps from DMSP-OLS data. Besides, in contrast to two maps from DMSP-OLS data, the emission map from NPP-VIIRS data is closer to the Vulcan inventory and exhibits a better agreement with the actual statistical data of CO2 emissions at the level of sub-administrative units of the United States. This study demonstrates that the NPP-VIIRS data can be a powerful tool for studying the spatial distributions of CO2 emissions, as well as the socioeconomic indicators at multiple scales.

Suggested Citation

  • Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    • Handle: RePEc:plo:pone00:0138310
    DOI: 10.1371/journal.pone.0138310
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    5. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    6. Ebrahim Hamid Hussein Al-Qadami & Zahiraniza Mustaffa & Mohamed E. Al-Atroush, 2022. "Evaluation of the Pavement Geothermal Energy Harvesting Technologies towards Sustainability and Renewable Energy," Energies, MDPI, vol. 15(3), pages 1-26, February.
    7. Yue Zheng & Jinpei Ou & Guangzhao Chen & Xinxin Wu & Xiaoping Liu, 2022. "Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England," IJERPH, MDPI, vol. 19(10), pages 1-22, May.
    8. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    9. Jeremy Proville & Daniel Zavala-Araiza & Gernot Wagner, 2017. "Night-time lights: A global, long term look at links to socio-economic trends," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-12, March.
    10. Robert Duerler & Chunxiang Cao & Bo Xie & Zhibin Huang & Yiyu Chen & Kaimin Wang & Min Xu & Yilin Lu, 2023. "Cross Reference of GDP Decrease with Nighttime Light Data via Remote Sensing Diagnosis," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    11. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Estimation of consumption potentiality using VIIRS night-time light data," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    12. Xueqi Wang & Shuo Wang & Gengyuan Liu & Ningyu Yan & Qing Yang & Bin Chen & Junhong Bai & Yan Zhang & Ginevra Virginia Lombardi, 2022. "Identification of Priority Areas for Improving Urban Ecological Carrying Capacity: Based on Supply–Demand Matching of Ecosystem Services," Land, MDPI, vol. 11(5), pages 1-24, May.
    13. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
    14. Jesús Crespo Cuaresma & Olha Danylo & Steffen Fritz & Martin Hofer & Homi Kharas & Juan Carlos Laso Bayas, 2020. "What do we know about poverty in North Korea?," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-8, December.
    15. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.

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