City- and county-level spatio-temporal energy consumption and efficiency datasets for China from 1997 to 2017

Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data are the two most commonly used indicators of gross domestic product (GDP) estimation. Few studies explore the potential of daytime satellite data for estimating GDP. This study demonstrates a linear support vector machine (Linear-SVM) model to estimate GDP over Hubei province and Guangdong province, China, in 2013 from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Also, a comparison of MODIS data with DMSP/OLS and NPP/VIIRS nighttime light data was conducted. Results show that the Linear-SVM model (Hubei: R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.66, 0.71, 0.92; Guangdong: R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.37, 0.32, 0.67) has better model performance than simple linear regression (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.54, 0.59, 0.86; R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.23, 0.23, 0.63) based on DMSP/OLS nighttime lights, DMSP/OLS corrected nighttime lights, and NPP/VIIRS nighttime lights, respectively, while MODIS data has model performance of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.77 (Hubei) and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.55 (Guangdong) based on the Linear-SVM model, further indicating that MODIS data improves the accuracy of GDP estimation compared to DMSP/OLS nighttime lights. In addition, MODIS data produced finer GDP estimation than DMSP/OLS nighttime lights, especially in dark and light saturated areas. Although MODIS data is not as accurate as the NPP/VIIRS nighttime lights for estimating GDP, the proposed method could be applicable to other daytime satellite data and has broad prospects for improving the spatial and temporal resolution of regional economic activity and improving estimation accuracy.

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.

&amp;lt;p&amp;gt;Typhoon and windstorm induced extreme winds (e.g., daily maximum wind speed, DMWS) cause enormous economic losses and deaths in China every year, and rapid urbanization increased surface roughness might play a key role in extreme wind speed variability. Here, observed near-surface (at 10 m height) DMWS from 115 meteorological stations and combined DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) nighttime light data from 1992-2016 in Yangtze River Delta, a rapidly urbanized area of China, were used to analyze the impact of urbanization on DMWS variability. Raw wind speed observations were subject to a robust quality control and homogenization protocol using the Climatol package. The stations were firstly classified into six urbanized groups by the difference of nighttime light indices of each station between 1992 and 2016. The results show that DMWS in Yangtze River Delta has significantly (p &amp;lt; 0.05) declined by -0.209m s&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;decade&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; annually, with negative trends in most seasons, particularly in winter (-0.470 m s&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;decade&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;, p &amp;lt; 0.05) and autumn (-0.300 m s&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;decade&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;, p &amp;lt; 0.05), followed by spring (-0.178 m s&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;decade&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;, p &amp;gt; 0.10), while a weak increase in summer DMWS was found (+0.002 m s&amp;lt;sup&amp;gt;-1 &amp;lt;/sup&amp;gt;decade&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;, p &amp;gt; 0.10). The stations in the highly urbanized group show a higher magnitude in the decline of annual DMWS, indicating the key role of urbanization in weakening DMWS. Further, this is confirmed by the regional climate model (RegCM4) sensitive experiments conducted with different land use and cover data, that is, DMWS in 1992 was higher in the experiment using the real land use and cover data than in the experiment using the land use and cover data in 2016.&amp;lt;/p&amp;gt;

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) 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 CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 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 CO2 emissions dataset (PKU-CO2) 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 CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 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-CO2 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 - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data have been widely used to monitor human activities and urbanization. However, the DMSP-OLS sensor has no on-board calibration, the DMSP-OLS and NPP-VIIRS data are not spatially consistent and continuous due to the differences in spatial resolution and sensor design between satellites, which makes it difficult to use both datasets at the same time for spatio-temporal consistency analysis. Based on this, this study proposed a new approach for systematically calibrating the DMSP-OLS and NPP-VIIRS NTL data, and rapidly generated a continuously consistent NTL dataset from 1992 to 2022. First, the DMSP-OLS data were calibrated using the invariant target method. Secondly, the NPP-VIIRS data were subjected to outlier elimination and time-series comparability calibration. Third, a new fourth-degree polynomial fit calibration model is proposed to calibrate the NPP-VIIRS data into the DMSP-OLS data scale. Finally, this research obtained a long-time series (1992–2022) “DMSP-OLS-like” NTL dataset, and analyzed the dynamics NTLs at different scales. Compared with other fitting methods, the “DMSP-OLS-like” dataset in this research has a clearer city hierarchy. It significantly reduces the saturation phenomenon and spillover effect, has a good spatial pattern and spatio-temporal consistency, and is highly compatible with the relevant socioeconomic reference quantities. The correlation coefficients <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of total DN with population, GDP, and electricity consumption were 0.922, 0.922, and 0.988, respectively. The results showed that the proposed approach was effective, which is superior to those of existing researches. Our results effectually solved the problem that the two NTL datasets could not be used at the same time, and improved the calibration accuracy in the two NTL datasets, which provides a new source of data for relevant studies such as urban and environmental issues in long-time series.

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P &lt; 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data.

Efficiency of China's urban development under carbon emission constraints: A city-level analysis

Abstract. The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

Decomposition of energy efficiency and energy-saving potential in China: A three-hierarchy meta-frontier approach

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities.

Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) provide information on nighttime luminosity, a correlation of built environment and energy consumption. This research intends to estimate spatial distribution of electricity consumption (EC) in mainland China, and analyze the temporal and spatial change of electricity consumption during 2000–2012. Nighttime light vegetation index (NVI), ratio nighttime light vegetation index (RNVI), difference nighttime light vegetation index (DNVI), normalized difference nighttime light vegetation index (NDNVI), soil adjusted nighttime light vegetation index (SANVI), and modified difference nighttime light vegetation index (MDNVI) were used to compensate for shortages in DMSP/OLS data. Moderate resolution imaging spectroradiometer (MODIS) NDVI products, China GIS database, and socioeconomic statistical data were also considered. An EC estimation model was used to obtain EC during 2000–2012. We divided EC into four ratings and analyzed spatiotemporal patterns using exploratory spatial data analysis tools (e.g., Moran’s I and local indicators of spatial association-LISA statistics). Then we built a linear regression model of EC, and correlated with DMSP/OLS data to produce China’s EC spatially. We used mean relative error (MRE) to compare our results and related research outcomes. Our result showed lower MRE, i.e., superior accuracy. EC grew quickly in China from 2000 to 2012 increasing from 6.79 to 14.82 M kWh. Generating capacity and EC of 32 provinces, municipalities and autonomous regions have a strong spatial correlation. The proposed index combines information from DMSP/OLS NTL data and MODIS NDVI data for more detailed characterization of nighttime luminosity, and reduced NTL saturation. The index simplicity enables rapid characterization and monitoring of EC.

Satellite-derived nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) have been extensively used for monitoring human activities and urbanization processes. Differences of these two datasets in their spatial and radiometric properties make it difficult for a temporally consistent analysis using these two datasets together. In this article, we developed a new approach to integrate these two datasets and generated a temporally consistent NTL dataset from 1992 to 2018. First, we performed the pixel-level spatial resampling of VIIRS data using a kernel density method after preprocessing the raw VIIRS data. Second, we conducted a logarithmic transformation of the aggregated VIIRS data. Third, we proposed a sigmoid function between DMSP and processed VIIRS data to characterize their relationship. Using the proposed method, we generated a series of consistent DMSP NTL data in Southeast Asia from 1992 to 2018 and analyzed the dynamic of resulted NTL at different scales. The evaluations based on profile curves, spatial patterns, scatter correlations, and histograms, of NTLs, indicate that our approach can achieve a good agreement between DMSP and simulated DMSP data in the same year. Our approach offers the potential for generating a time series of global DMSP NTL data from 1992 to present, which can contribute a more continuous and consistent monitoring of human activities and a better understanding of the urbanization process.