Authors: Tomohiro Oda*, Universities Space Research Association, Columbia, MD, USA/NASA Goddard Space Flight Center, Greenbelt, MD, USA, Miguel O. Roman, Universities Space Research Association, Columbia, MD, USA, Ranjay Shrestha, NASA Goddard Space Flight Center, Greenbelt, MD, USA/Science Systems and Applications Inc, Greenbelt, MD, USA, Zhuosen Wang , NASA Goddard Space Flight Center, Greenbelt, MD, USA/University of Maryland, College Park, MD, US, Rostyslav Bun , Lviv Polytechnic National University, Lviv, Ukraine/WSB University, Dąbrowa Górnicza, Poland, Thomas Lauvaux , Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yivette, France, Sha Feng , Pennsylvania State University, State College, PA, USA , Andrew Schuh , Colorado State University, Fort Collins, CO, USA, Andrea Gaughan , University of Louisville, Louisville, KY, USA, Forrest Stevens , University of Louisville, Louisville, KY, USA, Alessandro Sorichetta, University of Southampton, Southampton, UK, Shamil Maksyutov, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan , Lesley E. Ott , NASA Goddard Space Flight Center, Greenbelt, MD, USA, Steven Pawson , NASA Goddard Space Flight Center, Greenbelt, MD, USA
Topics: Remote Sensing, Spatial Analysis & Modeling, Urban Geography
Keywords: remote sensing, nighttime lights, climate, urban, greenhouse gas, cities
Session Type: Paper
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Cities are the major source of man-made carbon dioxide (CO2) emissions. Accurately quantifying the emissions from global cities and monitoring the emission changes are critical for carbon cycle science, as well as climate mitigation under the Paris Climate Agreement. Since 2009, we have been exploring the use of satellite-observed nighttime lights (NTL) as a proxy for man-made CO2 emissions. We built the global high-resolution fossil fuel CO2 emission model, ODIAC, that distributes national level emissions onto a global 1x1km field. The ODIAC global CO2 emission maps have been heavily used in carbon cycle science applications, such as atmospheric transport modeling and flux inverse estimation, and have been subjected to objective evaluation through comparison to observations. The simple NTL-based emission downscaling has been working surprisingly well, and yields reasonable agreements with locally-constructed emission data products. We further improved our emission downscaling by using NASA’s “Black Marble” (NBM) Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Environmental Product. We confirmed that the use of NBM significantly reduced the downscaling errors at city level. In our paper, we discuss the utility of NTL for mapping out man-made emissions and its significance in potential science-based emission monitoring applications under the Paris Climate Agreement, especially when combined with data from other satellites. We also discuss the challenges in emission modeling at policy relevant scales and how we could improve the spatial emission estimates by reducing the errors and uncertainties in the spatial emission modeling.