As urbanization and industrialization continue to expand globally,urban sprawl(US) has emerged as a significant challenge to sustainable development. While research on the relationship between urban sprawl and ecological environments is well-established,the effect of urban sprawl on electricity intensity(EUS) at meso and macro scales has received limited attention due to the lack of reliable data and methodologies. To address the gap, this letter examined EUS in 204 cities in China. We began by developing an urban sprawl index using nighttime light remote sensing data for quantifying US. Then, we employed a benchmark econometric model to quantify the relationships between US and electricity intensity. Our results demonstrated that the effectiveness of using nighttime light remote sensing data as proxies for identifying urban sprawl. Moreover, we found that the US coefficient (0.181) is significantly positive, suggesting that a higher degree of urban sprawl leads to lower efficiency in electricity utilization. Heterogeneity analysis also shows that the US coefficient is the largest in small cities (5.163), followed by large cities (4.344), medium-sized cities (4.188), and megacities (0.311), demonstrating EUS basically decreases with an increase in city size. These findings offered valuable insights for Chinese policymakers in developing effective strategies for sustainable urban development and energy conservation.

Reducing carbon dioxide (CO2) emissions has been a global concern for urban development. In recent years, while the Suomi-National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite (Suomi-NPP VIIRS) nighttime light (NTL) data have been widely used to estimate CO2 emissions, the Luojia 1–01 NTL data with finer spatial resolution have rarely been used for this purpose. Therefore, this letter estimated four types of sectoral CO2 emissions (i.e., urban residential, services, industrial, and transport) in Chinese cities by merging two sets of NTL data with functional urban zoning information. The results show that Luojia 1–01 data outperformed Suomi-NPP VIIRS data in estimating total CO2 emissions. Regarding the disaggregated estimation of CO2 emissions in the service, industrial, and urban residential sectors, Luojia 1–01 data surpassed Suomi-NPP VIIRS data. However, Suomi-NPP VIIRS data were better suitable for estimating the transport CO2 emissions than Luojia 1–01 data. We found linear regression more appropriate for estimating CO2 emissions in the service, transport, and urban residential sectors, but the power function regression was more suitable for estimating CO2 emissions in the industrial sector. Our results will help provide a scientific reference for selecting optimal NTL data as well as regression models to be used in estimating sectoral CO2 emissions, which are also essential for achieving China’s carbon emissions targets.

As a key region of natural and socioeconomic systems, the Himalayan region has an important impact on socioeconomic development, geopolitical situation, and climate change worldwide. However, as a result of the coronavirus disease 2019 (COVID-19) pandemic, trade channels among countries have been forced to close in the Himalayan region, which has a great impact on socioeconomic development. In this letter, satellite remote sensing nighttime light (NTL) images were used to evaluate the impact of the COVID-19 pandemic on socioeconomic activity exchanges in the Himalayan region from perspectives of nighttime lights of trade ports and channel nodes and interactive nighttime lights (INL). Results show that the total nighttime lights (TNTs) at trade ports showed a downward trend in fluctuation during the pandemic because of the pandemic blockade policy. Most of node TNTs on the channels are still growing during the pandemic, mostly because of the recovery and development of economies in countries. The INL model indicates that the pandemic has partly prevented socioeconomic activity exchanges between countries, particularly between China and other countries, but there has been a stronger interaction between domestic subunits. This letter provides new insight into the assessment of the socioeconomic development in the Himalayan region based on the NTL data.

The nighttime economy has always been regarded as an important part of the economy. Monitoring and evaluating the nighttime economic level is of great significance for promoting consumption and economic growth and optimizing industrial structure. However, it is difficult to evaluate the nighttime economy in China due to the data being unavailable. Hence, the objective of this study is to identify and evaluate the nighttime economy in China from different perspectives. First, a comprehensive nighttime economic index (CNEI) was constructed by integrating the nighttime light intensity and the points of interest data to represent the nighttime economic level. The CNEI was then verified using the business report data and socioeconomic statistical data. The results show that the CNEI is highly correlated with the verified data. We also found that Shanghai, Chengdu, Guangzhou, and Shenzhen have the highest CNEI values, and the CNEI values of southern cities are generally higher than those of northern cities. This is mainly because the differences in the lifestyles, climatic factors, and cultural customs in the north and south determine the nighttime economic activities. Counties with very high CNEI values are mostly located in the capital cities of each province. The spatial agglomeration at the county level performed more strongly than that at the prefecture level. The study will not only help better understand the nighttime economic level on different scales but also contribute to city-level policymaking on urban planning and economic development.

Remotely sensed nighttime light (NL) data collected by the Suomi National Polar-orbiting Partnership Satellite equipped with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor have proven to be effective for evaluating fossil fuel combustion carbon emissions (CEs). However, few studies have analyzed the relationships between NL and CE originating from different sectors. The effects of impact factors on the NL-CE relationship have not been thoroughly examined and compared. Utilizing the corrected annual composite average of NPP-VIIRS data (NTL), this letter individually investigated the relationships between the NTL and CE from all types of fossil fuels total CE (TCE); CE from gasoline, diesel oil, natural gas, and cement urban carbon emission (UC); and CE from raw coal, cleaned coal, other washed coal, briquette, and coke industrial carbon emission (IC) in China at the provincial level. The impact factors governing the NTL-CE relationship were also examined. The results showed that total NLs (TNLs) may be a more effective means for estimating UC than other types of CE but may not be a good proxy for IC due to the mismatch between their amounts and brightness. The${R}^{{2}}$values from TNL and TCE analyses were higher than those of TNL and IC within the eastern, central, and western regions. Meanwhile, we found that NTL could more accurately evaluate CE in urban areas with a large population size and a relatively developed social economy. Although the urbanization rate was the most important factor in the assessment of CE from NTL, China’s urbanization rate presented an inverted U-shaped impact on the NTL-CE relationship in the long run.

This letter presents a cloud removal method for reconstructing the missing information in cloud-contaminated regions of a high-resolution (HR) optical satellite image (HRI) using two types of auxiliary images, i.e., a low-resolution (LR) optical satellite composite image (LRI) and a synthetic aperture radar (SAR) image. The LRI contributes low-frequency information, and the SAR image contributes high-frequency information for restoring the HRI. The approach is implemented using structure correspondences established by sparse representation. Specifically, two dictionary pairs are trained jointly: One pair is generated from the HRI and LRI gradient image patches, and the other is generated from the HRI and SAR gradient image patches. Experimental reconstructions of cloud-contaminated regions in HR Thematic Mapper images are performed using three types of auxiliary images, i.e., MODIS 16-day composite only, SAR only, and both MODIS composite and SAR, respectively. It is shown that the MODIS composite or the SAR data alone are not sufficient to restore the missing HR information, whereas the combination of the two types of data can provide both low- and high-frequency information. The proposed approach can achieve a highly accurate result and has potential in areas where land-cover change may occur.