Remotely sensed nighttime lights(NTL) have become an important means of capturing the spatiotemporal dynamics of urban socioeconomic activity due to their high spatiotemporal resolution and sensitivity to human presence. However, most existing studies focused on stock characteristics of NTL, which limited the capture of incremental changes and thus hindered the characterization of microscale urban transformation processes. Our study investigated the spatial distribution and temporal evolution ofnighttime light intensity increment(NLII) from 2012 to 2023 across Chinese cities. The spatial distribution of NLII was first characterized using a concentric ring analysis, followed by Gaussian-based model fitting to reveal diffusion laws. Then, fitting parameters were compared across cities of varying sizes to examine differences in NLII diffusion. Results show that NLII exhibits a wave-shaped diffusion pattern, marked by initial intensification and subsequent radial decline, with peak values located in urban-rural transitional zones and gradually shifting outward. The Gaussian-based model captures this pattern effectively (average R²=0.82). Parameter analysis reveals distinct NLII diffusion patterns across city scales. Megacities show outward peak migration and stable internal increments, indicating a shift from edge expansion to infill-driven, polycentric diffusion. Medium-sized cities present balanced distributions with peaks in intermediate zones, reflecting a transition from core agglomeration to peripheral growth. Small cities concentrate near urban cores, with limited spatial spread and steep peripheral decline. Our study uncovers a wave-shaped diffusion law of NTL from an incremental perspective and elucidates scale-dependent NLII variations, providing a novel framework for characterizing urbanization micro-dynamics.

Global urban expansion presents a clear upward trend, reshaping urban socioeconomic frameworks and environmental dynamics. This study developed a multisource classification framework to accurately identify hillside urban expansion (HUE) across China, a region undergoing rapid transformation amidst diverse mountainous landscapes. Based on this foundation, we investigated the spatiotemporal evolution patterns of HUE and analyzed its influence on the urban thermal environment (UTE). Results show that leveraging a developed classification framework, the identified HUE exhibits stronger edge detail capture and higher patch integrity, achieving an overall accuracy of 78%. HUE in China shows a general trend of spatial expansion, predominantly dominated by low hillside urban expansion (LC) and moderate hillside urban expansion (MC). The relationship between HUE and land surface temperature (LST) is predominantly positive and exhibits variability. Specifically, LC maintains a stable positive correlation with LST, whereas MC and heavy HUE display varying relationships with LST. Our study presents an effective way for accurately identifying and evaluating HUE and its UTE effects. It provides a scientific reference for land use and spatial governance studies and holds notable implications for the progress evaluation with the UN’s Sustainable Development Goals by 2030, particularly SDG 11 (Sustainable Cities).

As China promotes green and high-quality urban development, the urban polycentric spatial structure (PSS) has become an important means for cities to achieve environmental sustainability. There have been extensive studies on the impact of PSS on haze pollution (HP) from various views, but few studies have been done on how HP affects PSS. Thus, this letter aims to explore the impact of HP on PSS. To achieve this, the urban polycentric indexes were developed at first from the remote sensing nighttime light perspective. Then, the econometric models were employed to examine how HP affects PSS in China’s 206 prefecture-level cities based on panel data. Results show that HP promotes urban development toward PSS significantly. Moreover, this effect is moderated by the total urban population (TUP). For cities with a TUP lower than 5.777 million, HP is more conducive to promoting PSS. Reasonable measures should be taken to alleviate HP, and the government should balance the development of various regions to achieve high-quality and sustainable development in polycentric cities.

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.

Timely and effective assessments of post-earthquake losses and reconstruction situations have a crucial guiding value for adjusting and deploying reconstruction plans in disaster-stricken areas. Remote sensing nighttime light technology, relying on its own light information, can effectively monitor changes in the intensity of nighttime lights in disaster-stricken areas. Thus, this technology can provide firsthand data for a prompt understanding of earthquake-induced losses and post-earthquake reconstruction situations. Taking the example of the Turkey–Syria earthquake that occurred on February 6, 2023, we employed nighttime light statistical analysis and concentric ring analysis to evaluate the post-earthquake losses and reconstruction situations. Results indicate that: (1) At the provincial level, during the post-earthquake period, Hatay and Şanlıurfa in Turkey were severely affected, with nighttime light intensity decreasing by 347,800 and 247,392 nWcm-2sr-1, respectively. During the post-earthquake reconstruction period, nighttime lights for all provinces recovered to or exceeded pre-earthquake levels, with the best recovery observed in Şanlıurfa Province, Aleppo Province. (2) At the county level, during the post-earthquake period, cities with the highest reduction in nighttime lights were Ar-Raqqah, Sanliurfa, Viranşehir, Dörtyol, and Ceylanpınar, with reductions of 112,267, 91,004, 65,630, 50,465, and 49,763 nWcm-2sr-1, respectively. During the post-earthquake reconstruction period, nighttime lights for the majority of areas recovered to or exceeded pre-earthquake levels. (3) The areas in close proximity to the earthquake’s epicenter were significantly affected by the event, but these areas exhibited the fastest recovery in post-earthquake reconstruction efforts.

Remote sensing nighttime light (NTL) data are widely used for fine-grained estimation of population due to their effectiveness in monitoring anthropic lights at night. However, there is a lack of research exploring the correlations between population and NTL data at various administrative and grid scales, as well as their related impact factors. Thus, to analyze the spatial effect of NTL on NTL-based population estimation, five models (linear, quadratic, exponential, logarithmic, and power function) were used in this letter to examine the correlations between NTL and population at multiscale by using the Visible Infrared Imaging Radiometer Suite data from the National Polar-orbiting Partnership. Results show that the correlations between NTL and population were significantly related to geographic scales. The power function model had the highest fitting accuracy between NTL and population when the grid scales were less than 5 km. The quadratic model had the highest precision on grid scales larger than 5 km. It showed a sharp increase in R2values between 0.5 and 10 km as the scale increase. At different scales, the correlations between NTL and population was negatively impacted by temperature, Normalized Difference Vegetation Index (NDVI), and Road Network Density (RND), but they were positively impacted by the Relief Degree of Land Surface (RDLS). We also found that RDLS had the greatest impact on model accuracy, followed by NDVI and temperature. In addition, RND had a low impact on the accuracy of population estimation on rough grids.

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.

Since February 2022, the Ukrainian-Russian conflict has become a focal point of international attention. Because remotely sensed nighttime light technology can effectively track variations in light intensity across war zones based on light information, data can provide first-hand information on the war’s development to improve understanding of its status. This informs policy decisions and facilitates post-conflict reconstruction. Thus, it is necessary to analyze spatiotemporal dynamics of the Ukrainian-Russian conflict using daily nighttime light data from the Black Marble product VNP46A2 of the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (SNPP-VIIRS). First, we developed a dataset of daily nighttime lights in Ukraine from February 24 to October 14, 2022. Then, statistical and spatiotemporal change analyses were used to detect spatiotemporal dynamics of nighttime lights in Ukraine from national, state, and city scales, respectively. The results show a significantly fluctuating decreasing trend of nighttime lights in Ukraine, indicating that the war greatly influenced socioeconomic development in the country. With the conversion of the core conflict locations in Ukraine, nighttime lights in Kharkov and Luhansk presented a significant decreasing trend. In some recovering states, nighttime lights remained low, indicating the challenges of socioeconomic recovery in the short term. This letter can provide an effective way for monitoring the trend of the Ukrainian-Russian conflict from a remotely sensed nighttime light perspective.

This study attempted to identify and quantify the morphology of intercity urban polycentric development (UPD) from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and urban land (UL) data sets in China at the provincial level. The spatiotemporal change and impact factors of UPD from 2000 to 2012 were also evaluated. The accuracy verification results indicated that the UPD could effectively and accurately identify and evaluate from the DMSP-OLS data and UL data sets in China. China’s urban structure presented a UPD trend from 2000 to 2012 and showed a pattern of high values in the eastern region and low values in the western region. In addition, the gross domestic product was proven to be a significant factor and had an inverted U-shaped impact on the UPD. The study can provide accurate time-series UPD data sets for decision-makers to evaluate the spatiotemporal change and driving mechanism of the intercity urban structure in China at the provincial level.

Satellite-based artificial nighttime brightness observations are typically considered proxy measures of socioeconomic indicators at large scales, such as population, gross domestic product (GDP), and carbon emissions. However, few studies have explored and compared the correlations between SNPP-VIIRS nighttime light data and socioeconomic indicators from administrative scale to grid scale, and further analyzed the potential mechanisms for the dissimilar correlations at different grid scales. Using regression model, dissimilarity index, and relief amplitude, the quantitative relationship and potential influence mechanism across different scales was investigated in this letter. Results show that the finer the scale is, the lower the correlations between total nighttime lights (NTL) and socioeconomic indicators when comparing 1 km, town, and county scales. The R2values of the NTL-socioeconomic indicator correlations increase sharply with the increase of grid scale at 1–10 km scale. The R2values increase volatilely between 10–30 km but are relatively stable above 30 km. The differences in R2values may be attributed to the diversity and distribution balance of industrial types and relief amplitude at different scales. This letter provides new insights into estimating and predicting population, GDP, and carbon emissions by using SNPP-VIIRS data.

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.

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.

Accurate information about the airport's throughput is crucial for monitoring traffic flow, evaluating the development status of aviation industry. In this letter, we used the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS) and the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light (NTL) data as effective proxies for evaluating and monitoring the airport's throughput power in China mainland. The results show that there is a significant positive correlation between the NTL intensity and the airport's throughput ( R2> 0.85). The NPP-VIIRS NTL data have been proved to not only distinguish the airport area and nonairport area but also build airport's NTL feature space. This letter reveals that the NTL images provide powerful remote sensing data sources to model the spatiotemporal dynamics of airport's throughput of China mainland at a large spatial scale.

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.

Super-resolution mapping of urban flood (SMUF) is one of the hotspots in remote sensing and urban environment research. In this letter, a new SMUF method based on the fusion of support vector machine and general regression neural network (FSVMGRNN) was proposed to achieve enhanced performance. An SVM-SMUF algorithm was developed and a fusion criterion was formulated. Then, the FSVMGRNN-SMUF algorithm was developed. The results of FSVMGRNN-SMUF were evaluated using Landsat 8 OLI imagery of two representative cities in China. FSVMGRNN-SMUF yielded the most accurate SMUF results among the five SMUF methods according to visual comparisons and quantitative comparisons. The mapping accuracy of FSVMGRNN-SMUF related to the kernel functions was also analyzed and discussed. The results of this letter will help to boost practical applications of median-low resolution remote sensing images in urban flooding mapping, and to strengthen the means for monitoring and assessing urban flooding disasters.

Accurate information on urban areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban areas. Various urban mapping methods have been proposed in the literature to extract urban built-up areas from the Defense Meteorological Satellite Program's Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban built-up area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban areas derived using each method against the referenced urban areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban built-up areas and nonurban areas. The selected methods for urban built-up area extraction were found to perform better on the log-transformed NTL data than the original NTL data.

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth's topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales.