The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country’s statistical data) at provincial- and prefectural-level divisions of mainland China. The result of the linear regression shows that R² values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (SNPP-VIIRS) data are valuable records of nighttime lights (NTLs) in analyzing socioeconomic development. However, inconsistencies between these data have severely restricted long time-series analyses. Published time-series NTL data sets are not widely available or accurate because the DMSP-OLS calibration is inadequate and some missing data in the SNPP-VIIRS data are seldom considered for patching. To address these issues, we calibrated DMSP-OLS data (1992–2013) by using a quadratic model based on a “pseudo-invariant pixel” method. Thereafter, an exponential smoothing model was used to predict and patch missing data in the monthly SNPP-VIIRS data (2013–2019). Outliers and noise were also removed from the annual data. In addition, a sigmoid model was employed to generate improved simulated DMSP-OLS (SDMSP-OLS) data (2013–2019), which were appended with the calibrated DMSP-OLS data (1992–2013) to develop improved DMSP-OLS-like data (1992–2019) in China. Finally, we qualitatively and quantitatively compared these data with published NTL data to examine data availability. Results showed that choosing invariant pixels to calibrate DMSP-OLS data can minimize discontinuity. The correlation between the SNPP-VIIRS data synthesized by the patched monthly SNPP-VIIRS data and the official annual SNPP-VIIRS data in 2015 ( $R^{2} =0.931$ ) and 2016 ( $R^{2} =0.930$ ) was higher than those of the two existing correction methods with $R^{2}$ values below 0.90. Spatial patterns of pixels in the improved SDMSP-OLS data in 2013 were more similar with the DMSP-OLS data than those in the published data. Strong correlations likewise existed between the total (average) pixel values of the improved SDMSP-OLS data (2013–2019) and the DMSP-OLS data in 2012. We also found that the improved DMSP-OLS-like data held strong linear correlations with different statistics, the average $R^{2}$ values of which were 0.931 and 0.654 at the national and provincial levels, respectively. Meanwhile, the average regression $R^{2}$ values between the two published data sets and statistics were 0.858/0.506 and 0.911/0.611, respectively. Our study has proven that the improved DMSP-OLS-like data (1992–2019) have immense potential to effectively evaluate socioeconomic development and anthropic activities.

China’s rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China’s total urban area increased from 31,076 km² in 2001 to 80,887 km² in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km² of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km² or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai–Nanjing–Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km²) and 6.05% (2702 km²) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km²) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China’s rapid urbanization.

The nighttime light (NTL) remote-sensing data have been widely applied in several applications for analyzing the urbanization process. The relationship between NTL intensity and human activity becomes a solid foundation for the applications using NTL data. However, there is no research, so far, revealing how the human activity seasonality could impact the seasonal change of NTL intensity. In this paper, a comparative analysis, box plot, and random forest algorithm were applied to NTL remote-sensing data and points of interest (POIs) data within Shanghai, China. The results show that in spring and autumn, the NTL is much brighter than that in summer and winter, especially within high human activity density area. The NTL intensity can be partly (approximately 40%) explained as the joint effects of the five POI categories. By analyzing the contributions of each POI category to NTL intensity, we found that the National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) could be used to dig more information about gross domestic product (GDP) and traffic-based applications with consideration of NTL seasonality.