The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

Abstract

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO 2 ) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO 2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO 2 emissions, and provide guidance and a reference for modeling CO 2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO 2 emissions dataset (PKU-CO 2 ) were used to perform simple regressions at provincial, prefectural and 0.1 • × 0.1 • grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO 2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO 2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO 2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO 2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO 2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights -CO 2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.

Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

“…Table 1 provides various parameters of DMSP-OLS and NPP-VIIRS nighttime light data, these parameters differ due to two different data sources. The spatial and radiometric resolution of DMSP-OLS data are lower compared to the NPP-VIIRS data [41]. The DMSP-OLS data do not have onboard calibration system, while the NPP-VIIRS data are calibrated [35].…”

Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives

“…The differences between DMSP-OLS and NPP-VIIRS have led to related studies for a better understanding of these two datasets and their differences. In a number of studies, the capability of NPP-VIIRS and DMSP-OLS have been compared in analyzing urbanization [79,221], socioeconomic activities [43,45,145,222], greenhouse gas emissions [158,223], and light pollution and its effects [204]. These attempts, which have emphasized the advantages of VIIRS data in detecting human activities at fine scales, have promoted new and improved methods to overcome the inherent deficiencies of DMSP data mentioned in Section 4.1.…”

Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data

“…In the field of spatial distribution analysis of CO 2 emissions, integrating the DMSP/OLS night-time stable light (NSL) data with CO 2 emissions data [20], and DMSP/OLS night-time light satellite data was applied to the following: analysis of the spatiotemporal distribution of CO 2 emission in China [20], estimation of the carbon dioxide emissions in China [21], assessment of China's city-level CO 2 emissions arising from energy consumption [22], estimation of CO 2 emissions at urban scales of China [23], estimation of CO 2 emissions in East Asian Region [24], development of a global 1 km×1 km annual fossil fuel CO 2 emission inventory [25], evaluation of the constraints on the spatial structure of CO 2 emissions from fossil fuels [26], use of imagery as a tool for global mapping of greenhouse gas emissions [27], creation of a global grid of distributed fossil fuel CO 2 emissions [28]. Results show that DMSP/OLS night-time light satellite data with CO 2 emissions data are good agreement, and can be extended to the future using updated data.…”