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Abstract The COVID-19 pandemic has highlighted the importance of understanding environmental factors in disease transmission. This study aims to explore the spatial association between nighttime light (NTL) from satellite imagery and COVID-19 mortality. It particularly examines how NTL serves as a pragmatic proxy to estimate human interaction in illuminated nocturnal area, thereby impacting viral transmission dynamics to neighboring areas, which is defined as spillover effect. Analyzing 43,199 COVID-19 deaths from national mortality data during January 2020 and October 2022, satellite-derived NTL data, and various environmental and socio-demographic covariates, we employed the Spatial Durbin Error Model to estimate the direct and indirect effect of NTL on COVID-19 mortality. Higher NTL was initially directly linked to increased COVID-19 mortality but this association diminished over time. The spillover effect also changed: during the early 3rd wave (December 2020 – February 2021), a unit (nanoWatts/sr/cm2) increase in NTL led to a 7.9% increase in neighboring area mortality (p = 0.013). In contrast, in the later 7th wave (July – September 2022), dominated by Omicron, a unit increase in NTL resulted in an 8.9% decrease in mortality in neighboring areas (p = 0.029). The shift from a positive to a negative spillover effect indicates a change in infection dynamics during the pandemic. The study provided a novel approach to assess nighttime human activity and its influence on disease transmission, offering insights for public health strategies utilizing satellite imagery, particularly when direct data collection is impractical while the collection from space is readily available.

[Objective] As a systemic transformation of the economic and social system, the low-carbon energy transition will inevitably impact social issues such as equity and income. Investigating the impact of city low-carbon energy transition on income inequality can provide valuable empirical insights for coordinating the low-carbon transformation of the economy and society and for narrowing income gaps. [Methods] This study used the panel data from 281 prefecture-level cities in China from 2007 to 2020 as the research sample, and established a multidimensional indicator system to calculate city low-carbon energy transition index. Based on the nighttime light data of 2109 districts and counties, the income inequality between different urban income groups was measured. Subsequently, the intrinsic relationship between low-carbon energy transition and income inequality was examined by both theoretical modeling and empirical analysis. [Results] (1) The low-carbon energy transition significantly alleviated income inequality, with each standard deviation increase in the transition index reducing income inequality by approximately 6.1%. (2) Mechanism analysis indicated that the low-carbon energy transition affected income inequality through skill-biased technological progress and the upgrading of the labor force’s skill structure, with the alleviating effect on income inequality mainly stemming from its role in enhancing the skill structure of the labor force. (3) Heterogeneity analysis revealed regional and urban characteristic heterogeneity in the impact of low-carbon energy transition on income inequality. (4) The research using the spatial Durbin model showed that the impact of low-carbon energy transition on income inequality had spatial spillover effects, which can reduce income inequality in neighboring areas. [Conclusion] Renewable energy should be vigorously developed, and regular exchanges and cooperation in energy transition between regions should be strengthened to fully leverage the transition’s role in upgrading the labor force’s skill structure, thereby achieving coordinated development that promotes low-carbon energy transition and narrows income gaps.

Nighttime light (NTL) is a reliable indicator for measuring urban development, population density, and economic activities. This study sets out to explain the spatio-temporal pattern of urbanization and population distribution using pixel-based NTL data. Changes in NTL intensity in Puglia (Italy) were evaluated from 2014 to 2023, and how NTL is affected by various spatial metrics was investigated. Summer–winter differences at the pixel level were monitored utilizing the Normalized Nighttime Light Index (NNLI), and the relationship between this indicator and tourism was assessed. A consistent increase in NTL across the region in both summer and winter was measured, with variations among provinces. Urban areas with high population density showed greater NTL values, while inland areas showed low NTL intensity. The study also revealed that proximity to urban areas was the most influential factor in predicting NTL intensity. The analysis revealed that differences between summer and winter correlated with tourist activity, especially in coastal municipalities. Leveraging NTL images can support decision-making across various sectors and aid in developing evidence-based urban strategies tailored to local needs and challenges.

The Tibetan Plateau, known for its high elevation and sparse population distribution, heavily depends on gridded population data to enhance disaster prevention and management strategies. This study utilizes multi-source physical geographic and socio-economic factors to delineate the population distribution across the plateau. Using data from the seventh National Census in 2020, we apply three individual machine learning methods (Random Forest, GBDT, and XGBoost) and two multi-model ensemble methods (weighted average ensemble and stacking ensemble) to spatialize the population data into a 100-meter grid. The results reveal that the spatialization accuracy of all models exceeds that of the WorldPop dataset. Specifically, the Random Forest model (RMSE = 4061.09, nRMSE = 44.71 %) and the stacking ensemble model (RMSE = 4094.47, nRMSE = 44.26 %) demonstrate the highest accuracy among the individual and ensemble models, respectively. Emphasizing the importance of integrating multi-source big data, Tencent location-based services data emerges as a crucial variable across all models. This study highlights the effectiveness of ensemble models and multi-source big data in improving population mapping accuracy, especially in regions with complex terrains.

Impervious surface extraction is essential for environmental management and urban studies. Although nighttime light data have been widely used for this purpose, few studies have utilized the high-resolution (21 m) Qimingxing-1 data released in 2022, which offer significantly higher spatial resolution compared to the widely used NPP-VIIRS product (750 m). In this study, the effectiveness of Qimingxing-1 nighttime light data for impervious surface extraction was investigated for the first time using the highly urbanized city of Dongguan as an example. We combined nighttime light data with POI data to create a ‘Nighttime-POI’ (NP) index and extracted impervious surfaces from four datasets (Qimingxing-1, NPP-VIIRS, QM_NP, and NPP_NP) using the thresholding method. Comparisons with reference data from Landsat 8 and Sentinel-2 revealed that the Qimingxing-1 data improved extraction accuracy by 2.7% over NPP-VIIRS. The Qimingxing-1 data could more accurately reflect the spatial distribution of impervious surfaces and imperviousness degree. Additionally, combining nighttime light data with POI data further enhanced accuracy by 2–3%, resulting in more accurate boundaries and detailed information on impervious surfaces. In conclusion, the Qimingxing-1 data are highly advantageous for impervious surface extraction. The high-precision impervious surface results can assist the government in promptly comprehending the dynamics of urban development.

Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

ABSTRACTThe Central Business Districts (CBDs) are important hubs for urban economic activities. In the context of globalization, a unified CBD boundary would greatly facilitate the study of global CBD comparative analysis, urban socio-economic development, urban transport and commuting mode. However, previous studies have encountered challenges in developing a practical method for global CBD identification due to limitations in data sources and methodologies. In this study, we selected 32 global megacities as research objects and used the open-access Black Marble nighttime light (NTL) products to construct indicators with the intensity and angular effects of NTL. Clustering and decision tree were then employed to derive rules for CBD identification. Results show that combining Z-score indicators and the strategy of clustering 32 cities before decision tree classification could improve the accuracy of CBD identification, which achieved a producer accuracy of 85%. The 32 cities were clustered into three types, i.e. U.S.A.-like, the mixed type, and China-like. The rules for CBD identification became more complex in the above order, but the accuracy decreased in turn. This study provides a new CBD identification method for cities lacking reference data, allowing for the delineation of unified and comparable CBD boundaries on a large scale.

ABSTRACTUnderstanding the urbanization and transformation trajectories of resource-based cities (RBCs) is pivotal for China’s sustainable development goals. This study introduces a novel regression-based algorithm for assessing urbanization patterns. We delineated the urban boundaries of 335 cities and employed time-series nighttime light data from 2001 to 2020, shifting the analysis from a pixel-scale to an urban-scale perspective. Our analysis reveals distinct disparities within the 125 RBCs when compared to the national average, leading to their categorization. The key findings include: (1) Within Forest and Coal RBCs, numerous areas have stabilized after experiencing contraction. Geographically, a significant number of RBCs in the Northeast, North, and Northwest regions are experiencing or have experienced substantial urban shrinkage. The developmental status of RBCs exhibits a spatial positive correlation. (2) Although the government categorized RBCs based on their development level in 2013, findings suggest that this classification may no longer accurately reflect the current development status of RBCs in the context of urbanization. (3) Utilizing spectral clustering, we categorized RBCs into five types, identified 10 RBCs undergoing shrinkage and 30 cities trending towards stabilization post-shrinkage. This research offers a refined evaluative method for urbanization, providing insights beneficial for policy-making concerning RBCs’ sustainable growth..

ABSTRACTDue to the difficulty of obtaining survey data, nighttime light (NTL) imagery has emerged as valuable alternative data for asset wealth estimation. However, the nighttime light values do not differentiate between levels of asset wealth in various unlit areas, as the nighttime light values in unlit areas are all 0. Here, NTL data and World Settlement Footprint (WSF) data were combined to extract multidimensional luminous features that are also differential in unlit areas to estimate asset wealth in nighttime light-poor areas at 500 m × 500 m spatial units. A random forest model was used to estimate asset wealth, based on the shortest distance of settlements to three categories of lighted areas, along with the brightness values derived from the nearest lighted area and the settlements themselves. This model achieved an explanation of 71% for the variation in settlement asset wealth and demonstrated effectiveness in estimating the asset wealth of unlit areas. The MAE and RMSE of asset wealth estimation in the unlit clusters were 4.03 and 5.28, respectively. Asset wealth is generally low across most African settlements, with clear two-tier differentiation in Africa. In summary, the proposed method can extensively explore the luminous information in unlit areas.

Abstract The global COVID‐19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID‐19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID‐19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID‐19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID‐19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre‐lockdown levels, while in the sub‐center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub‐center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID‐19 lockdown on economic activities in the Zhengzhou region.

The intensity of extreme weather events has been increasing, posing a unique threat to society and highlighting the importance of our electrical power system, a key component in our infrastructure. In severe weather events, quickly identifying power outage impact zones and affected communities is crucial for informed disaster response. However, a lack of household-level power outage data impedes timely and precise assessments. To address these challenges, we introduced an analytical workflow using NASA’s Black Marble daily nighttime light (NTL) images to detect power outages from the 2021 Winter Storm Uri. This workflow includes adjustments to mitigate viewing angle and snow reflection effects. Power outage is detected by comparing storm-time and baseline (normal condition) NTL images using an empirical adjusted equation. The outcomes of the workflow are 500-meter resolution power outage maps, which have the optimal correlation with real outage tracking data when NTL intensity is reduced by 26%. With the resultant power outage maps, we analyzed the relations between power outages and disadvantaged populations in 126 Texas counties and 4182 census tracts to evaluate environmental justice in the storm. The results show that Latino/Hispanic communities tend to suffer more from power outages at both the county and census tract levels.

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China’s population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.

The local climate zone (LCZ) scheme has been widely utilized in regional climate modeling, urban planning, and thermal comfort investigations. However, existing LCZ classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments. In this study, we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations, i.e. daytime Ziyuan-3 stereo imageries (2.1 m) and Luojia-1 nighttime light (NTL) data (130 m). Firstly, we performed land cover classification using multiple machine learning methods (i.e. random forest (RF) and XGBoost algorithms) and various features (i.e. spectral, textural, multi-view features, 3D urban structure parameters (USPs), and NTL). In addition, we developed a set of new cumulative elevation indexes to improve building roughness assessments. The indexes can estimate building roughness directly from fused point clouds generated by both along- and across-track modes. Finally, based on the land cover and building roughness results, we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping. The results for Beijing, China, show that our method yielded satisfactory accuracy for LCZ mapping, with an overall accuracy (OA) of 90.46%. The overall accuracy of land cover classification using 3D USPs generated from both along- and across-track modes increased by 4.66%, compared to that of using the single along-track mode. Additionally, the OA value of LCZ mapping using 2D and 3D USPs (88.18%) achieved a better result than using only 2D USPs (83.83%). The use of NTL data increased the classification accuracy of LCZs E (bare rock or paved) and F (bare soil or sand) by 6.54% and 3.94%, respectively. The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

A major source of carbon dioxide emissions (CO2) arises from the household sector. Recent studies have reported increasing household CO2 emissions (HCO2) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO2 if spatial and temporal variations in HCO2 are understood at fine scales. Here, we applied panel data analysis to map HCO2 at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO2 were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO2 growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO2 (T-HCO2). The results indicated that T-HCO2 has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO2 through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO2 through two or more of the growth modes, confirming a significant variation in HCO2 across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO2. We recommend increased efforts to encourage HCO2 mitigation in cities that have contributed to the rise in T-HCO2 to help neutralize carbon emissions at the national level.

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability.

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection.

Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies.

The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally.

The extent of human activity has expanded, and the intensity of human activity has increased, supported by a progressive economy and powerful technology. Compared with daytime remote sensing images or statistical data, nighttime light (NTL) remote sensing has the advantages of long time series, global coverage, objectivity, and quantifiability in observing the development of global human activities. Existing studies have focused more on the levels of human activity development at multiple points in time and less on overall patterns of human activity development during a period. We developed a non-parametric time-series curve morphology classification methodology combining trend analysis and change-point detection. We identified modes and phases of human activity development based on NTL datasets and analyzed the spatial and temporal patterns, modes, and phases of county-level human activity development globally over the past 30 years (1992–2021). We found that the level of human activity development is generally on the rise globally, with areas of significant growth mainly along the coast of China and in global ports. In more than half of the counties, the extent of human activity development is saturated. A significant increase mode was observed in most counties (71.5 %), while a few counties (1.1 %) saw a decrease mode. 10.1 % of global counties are in an anomalous phase, with low levels of human activity and slower or even stopped development. The new method has been validated in cases of different development modes and has the potential to generalize to the pixel level. Monitoring human activity development facilitates understanding development modes globally over a long time series, which can guide future policy formulation and socioeconomic progress. We recommend a greater focus on inequalities within and between countries.

ABSTRACTNighttime light (NTL) remote sensing data plays a crucial role in comprehending changes in human activities. The availability of the daily lunar BRDF-corrected Black Marble NTL product (VNP46A2) enables the use of NTL data to detect and assess the impact of short-term emergencies. However, daily NTL data often experience missing values due to cloud cover and low-quality signals. To address this issue, many studies utilize monthly or annual time-composite NTL products, which restrict the timeliness and potential application scenarios of NTL data usage. Therefore, it is necessary to generate the gap-filled daily NTL product. This study presented a novel NTL gap-filling method comprising rough reconstruction based on spatiotemporal weighting and refined gap-filling using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. We evaluate the accuracy of the proposed method using the “remove-reconstruct-compare” approach, which randomly removes some original data from the complete image, fills the gaps with the proposed gap-filling method, and compares the reconstructed NTL data with the original observations in Beijing, Shanghai, Xi’an and New York. The results reveal that when the rate of missing values in Beijing is 40% and 50%, the proposed gap-filling method achieves accuracy with mean coefficient of determination (R2) values of 0.834 and 0.841, accompanied by corresponding root mean square (RMSE) values of 7.793 and 7.171, respectively. Furthermore, the gap-filling accuracy was evaluated quantitatively, and our proposed gap-filling method performed better than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Our proposed gap-filling method had R2 values of 0.685, 0.781, 0.720 and 0.642, which were higher than those for STARFM (0.430, 0.662, 0.221 and 0.345). The RMSE values of our gap-filling method were 9.628, 12.083, 10.963 and 19.882 for the four sites, while those of STARFM were 12.953, 14.872, 18.280 and 26.990, respectively. The temporal and spatial analysis results demonstrate that this model is robust, capturing city boundaries and NTL high-brightness hotspots with high accuracy and stability. The gap-filling model proposed in this study provides a new technique for expanding the potential applications and reliability of NASA’s daily Black Marble product (VNP46A2) in remote sensing.

In China, tourism development is a crucial approach to poverty alleviation. With the consolidation of poverty alleviation achievements and the promotion of rural revitalization, it is of great significance to explore the relationship between tourism development and poverty alleviation from the perspective of multidimensional poverty. Therefore, this study took 28 key assistance counties for rural revitalization in the Sichuan–Chongqing region (hereinafter referred to as “key counties”) as the research objects, introduced NPP-VIIRS nighttime light (NTL) data, and a coupling coordination degree (CCD) model to explore the coordination relationship and mechanism between them. The results showed that from 2015 to 2020, the tourism development index (TDI) and estimated comprehensive development index (ECDI) of the key counties increased by 112.57% and 115.12%, respectively. In addition, the spatial differences in tourism development and multidimensional poverty both showed a narrowing trend. According to the results of the CCD model, the key counties basically faced coordination obstacles in the early stage, which were mainly transformed into reluctant coordination and moderate coordination in the later stage. This indicated that tourism poverty alleviation showed a coordinated development trend overall. However, the study also found that there may not be synchronicity between tourism development and poverty alleviation and analyzed the mechanism of their interaction. Overall, the study confirmed the positive impact of tourism development on alleviating multidimensional poverty. In addition, the study found that measuring multidimensional poverty based on NTL data has a high accuracy and can provide support for poverty research. These research results have an important reference value for China to carry out sustainable tourism poverty alleviation and comprehensively promote rural revitalization.

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Introdution: One crucial method to attain Sustainable Development Goals (SDGs) involves timely adjustment of development policies, promoting the realization of SDGs through a time-series assessment of the degree of accomplishment. In practical applications, data acquisition is a significant constraint in evaluating the SDGs, not only in China but across the globe. Hence, expanding data channels and exploring the feasibility of various data sources for sustainable development assessment are effective strategies to tackle the challenge of data acquisition.Methods: In light of this issue, this study selected Nighttime Light Data, a remote sensing data source closely linked to human social activities, as an alternative data source. Using Yunnan Province as an example, 16 localized indicators of social, economic, and environmental types were chosen. These indicators were then subjected to a correlation analysis with the Total Nighttime Light Index (TNLI). The relationships between different types of indicators and TNLI were analyzed at both temporal and spatial scales, thus identifying the indicators for which TNLI could serve as a suitable substitute measure.Results: The study indicates that when the SDG indicators are classified into economic, social and environmental categories, the total value of nighttime light presents a significant correlation and substitutability with economic indicators; significantly correlated with some social indicators, it can reveal the weak links in the development of underdeveloped areas; it is not significantly correlated with environmental indicators, while a trend correlation exists, which can provide some reference values.Discussion: This study has demonstrated the feasibility of using Nighttime Light Data for sustainable development assessment. It provides a novel evaluation method for countries that, despite a lack of resources for conducting sustainable development assessments, have a greater need for such assessments due to their lower economic development. Furthermore, a multitude of assessment methods can be developed based on Nighttime Light Data.

The comprehensive characterization of global urbanization requires consideration of both human activities and urban physical structures. Both human activities and urban physical structures exhibit regular self-similar patterns, yet the spatial patterns between the two at a global scale remain elusive. This study utilized NPP-VIIRS annual composite data and newly available world settlement footprint 3D data to investigate the global spatial relationships between nighttime light intensity and urban building morphological indicators across several spatial scales. Our results demonstrated that there is a weak association between nighttime light intensity and urban building morphology at the pixel level, as shown by a maximum correlation coefficient of approximately 0.4, but a strong correlation at the provincial/state level with a correlation coefficient over 0.8. Additionally, we performed an urban-rural gradient analysis to evaluate the spatial patterns between nighttime light intensity and urban building morphological indicators. The results indicated that the dominant urban-rural gradients for both nighttime light intensity and building morphologies follow a declining trend from urban centers to rural areas. Notably, spatial inconsistencies between nighttime light intensity and building morphology were found predominantly in Africa. Our findings also suggested that spatial patterns between nighttime light intensity and urban building morphology can be served as an indicator of urbanization, and thus can provide implications for facilitating solutions aimed at reducing income disparity and promoting sustainable urban development.

Remotely sensed light imagery provides a unique perspective for high-frequency human activity monitoring. NASA has developed the Black Marble, a daily nighttime light (NTL) product from the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime images by correcting environmental effects (i.e., atmospheric, lunar, and stray light effects). However, variation and uncertainty remain in the daily lunar-BRDF corrected NTL product and hinder its quantitative applications. To better understand the uncertainty across multiple spatial and temporal scales, this study proposed a spatial–temporal hierarchical analysis strategy to separate uncertainties from different sources, and evaluated the effects of multiple factors on daily NTL time series. The experiments conducted on two populous regions show that: (1) The daily NTL in Northern America has variations up to 50% of the annual average, which is stronger than in East Asia, with variations up to 25%. (2) The variation in seasonality is stronger in Northern America, whereas the variation related to day-to-day changing factors is stronger in East Asia. (3) Generalized linear models are built to capture the relationship between daily NTL and influential factors in each grid, revealing that approximately 25%∼50% of the daily variations can be explained by environmental factors, observational factors, and seasonality. (4) Environmental factors and observational conditions show spatially varying impacts on NTL. Specifically, aerosol exhibit opposite impacts on rural and urban areas; significant impacts of moonlight are mainly distributed in rural areas; the impact of satellite viewing angle is less pronounced and frequent in East Asian cities compared to those in North America. This research revealed the essential knowledge about variations of satellite-derived NTL and will benefit the processing and utilization of daily NTL products.

China fully lifted restrictions of the COVID-19 pandemic on December 7, 2022. However, the influence of this policy on urban socioeconomic activities is unclear. Remotely sensed nighttime light (NTL) data have been widely used in evaluating public policies in near real time; therefore, this article attempts to explore the spatiotemporal impact of liberalization on mainland China from the perspective of NTL data. Taking 25 cities with different development levels as representatives, based on daily Black Marble NTL product (VNP46A2), we obtained monthly and weekly averaged NTL images before and after liberalization, and calculated NTL changes in concentric rings to detect the spatiotemporal variations from the urban centers, which can reflect the urban vitality to some degree. Experiment results show that urban NTL radiances within 18 km from urban center are generally on the rise after the lifting of restrictions. The increase of NTL around urban core is the most significant, and the increase intensity decreases exponentially (R2 > 0.7) with the distance from city center or near-center for most cities. Furthermore, NTL radiance generally increases significantly within 1–2 weeks after the lifting in megacities, and different levels of cities responded differently. NTL of megacities and large cities generally changed more rapidly than small–medium cities. Moreover, although mainland China has experienced 3 years of pandemic control, urban development is still underway. The lifting of COVID-19 travel restrictions has brought back the recovery of urban economic vitality. These findings can provide insightful support to urban construction and policy guidance intervention after the pandemic.

The urban functional areas (UFAs) are the basic spatial units of a city, identifying their category information and spatial distribution is of great significance for studying urban spatial structure and formulating scientific and reasonable urban planning policies. However, traditional classification systems are still limited due to the data processing costs and time-consuming process. With the development of high-resolution satellite, the 2-D image has been able to identify more ground object information on a fine scale, but it lacks the application of 3-D characteristics and social attributes. At the same time, machine learning and deep learning have shown great utility in extracting features, and they should be applied more to the classification of urban functional areas to improve efficiency. To solve these problems, we propose an efficient and accurate framework for mapping UFA using multisource geospatial data. It can grasp the dynamic changes in the city more accurately, and provide a reference for the study of urban functional areas. An improved frequency density (IFD) model was proposed to improve the overall classification accuracy by 4.4%. Besides, the nighttime light (NTL) data from SDGSAT-1 satellite glimmer imagers are also used to classify UFA. By combining NTL and urban building height data, a sky view enhanced nightlight index (SVENI) was proposed, which can improve the overall classification accuracy of UFA by 5.8%. This study systematically clarifies the role of data sources, methods, and automatic integration models in the classification framework for UFA, which is of great significance for urban planning and sustainable development.

Rapid urban expansion reflects the increasing economic growth in China. As an original conceive for urban sustainability, economic growth contributes to vegetation changes. However, the spatiotemporal heterogeneity of the relationships between these two factors remains unclear in China. After analyzing the spatiotemporal characteristics of the urban expansion in different cities in China from 2002 to 2020, we found a rapid and continuous urban expansion in China, with the mean annual expansion of 1710.96 km2 year−1 (p 60% of cities showed incoordination status and 38.08% of cities showed strongly incoordination status. At pixels levels, these strong incoordination status in urban areas can be fully explained by long-term average NTL digital Number (DN) gradient.Based on a piecewise linear regression model, the proportion of areas with the strong incoordination status was significantly increased by the 1.07% DN−1 (p

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.

Mangrove forests are one of the most biologically diverse and productive ecosystems on Earth. They are important breeding and nursing grounds for amphibians, invertebrates, birds, fish, etc. Light pollution may cause serious degradation of biodiversity in the ecosystem. A report of the long-term holistic views of light pollution changes is essential for sustainable management of mangrove ecosystems. However, to date, such studies have rarely been carried out. This study aimed to monitor the long-term light pollution changes of China’s mangrove forests. To achieve this goal, we used time-series nighttime light (NTL) data to build continuous light pollution data. NTL maps made with DMSP-OLS (DNL) or NPP-VIIRS (VNL) are widely utilized in research on human activity. However, DMSP and VIIRS images are different in spatial resolution, radiation resolution, and data saturation. Thus, this study innovatively set an optimal threshold for generating consistent light pollution data in mangrove areas from 1992-2020. The results showed that: (1) the proportion of light-polluted mangrove forests in China increased from 12% in 1992 to 52% in 2020; (2) the largest net increase occurred in Guangxi with an area of 4,086 ha, followed by Guangdong (3,365 ha) and Hainan (2,944 ha); (3) Zhejiang had the largest proportion of net growth (from 0% in 1992 to 99% in 2020), followed by Hainan (66%) and Fujian (59%). Mangrove forests have been protected and restored for decades in China; this study indicates that the establishment of nature reserves is effective in preventing the light pollution of mangroves and provides the first long-term multi-temporal dataset of light pollution in China’s mangrove forests. This comprehensive information could support related studies and facilitate the development of applicable coastal management strategies in China.

Rapid urbanization has placed the sustainable development of some watershed ecosystems in jeopardy. In order to achieve sustainable urban development, it is vital to identify the coupling mechanisms between urbanization and the ecological environment quality. This study establishes indicators to evaluate the Jiulong River Basin’s urbanization and ecological environment systems. These are utilized to analyze spatial and temporal changes and build a coupling coordination degree model. This research investigates the level of development coordination between urbanization and the ecological environment quality in the basin. The data sources include nighttime lighting and Landsat data from 2000 to 2020. The findings indicated the following: 1) Urbanization levels in the basin rise annually, and the years 2010–2020 represent a stage of high urbanization growth. In addition, the development levels are spatially heterogeneous, with high levels in the south and low levels in the north. 2) The ecological environment quality category for the basin is generally Excellent; however, many facets of the climate and human activity drastically lowered this grade in 2005. 3) In the basin, there is a basic coordination relationship between urbanization and environmental quality, but the number of cities falling into the moderate imbalance category has grown. 4) Increasing the urbanization level raises the coordination between urbanization and the ecological environment more than improving environmental quality, but antagonistic effects make it necessary to strengthen the protection of the ecological environment alongside economic development. From the viewpoint of counties, this study examines the long-term interactions between the ecological environment and urbanization in Fujian Province, China. Recommendations for balancing urban development and environmental concerns in coastal watersheds are presented, providing references to the fact that the future of this area of China can be sustainable.

Long-term nighttime light data can effectively reflect the spatiotemporal evolution pattern of urban areas. Based on nighttime light data obtained from 1992 to 2021, the spatiotemporal expansion modes of urban areas on the Loess Plateau were investigated by remote sensing image band fusion processing, geospatial analysis, statistics and classification methods. Results show that the characteristics of the spatiotemporal expansion of urban areas were retained in the extraction boundaries. From the perspective of time scales, the urban areas have grown tripled to 11,481 km2. Four significant instances of urban areas increase occurred, with one large increase occurring almost every ten years. From the perspective of spatial scales, urban area expansion has two important features. First, the expansion speed of urban areas, which is based on three indicators, has large spatial dissimilarities. Five modes can be divided based on the expansion velocity of 44 cities/counties. Second, urban development increases uniquely, and can be described based on three expansion modes: Ring Extension, which mainly occurs in wide and flat basins of the main rivers; Linear Extension primarily arises on both banks of rivers in hilly and gully areas; and Point Extension mainly occurs in areas away from the main rivers. Our results highlight that the spatiotemporal expansion modes of urban areas on the Loess Plateau retain the significant influence of the geological environment, which can provide scientific guidance for the sustainable development of human-land relationships.

Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies.

Human activities are one of the main factors threatening the long-term survival of wild giant pandas. Long-term and large-scale human activities can be quickly assessed by night light remote sensing data, which has been more and more widely used. In this paper, we analyzed the spatial patterns of nighttime light intensity and their dynamic changes from 2000 to 2020 in the entire giant panda distribution area using long-term nighttime light remote sensing data, and evaluated the dynamic threats of nighttime light-represented human activities to giant panda populations by proximity of light source and its intensity. We also assessed the relationships between the nighttime light dynamics and the trends of habitat fragmentation. Results showed that the intensity and range of the nighttime light around panda habitat had increased significantly from 2000 to 2020. The nighttime light intensity inside the natural reserves is significantly lower than outside, and it becomes more obvious over time. The intensity and range of nighttime light inside the natural reserves first increased and then decreased during the two decades, indicating that the protection inside the natural reserves effectively reversed the rapid increase in human activities in the previous decade. From 2000 to 2020, nighttime light sources became closer to local panda populations. In particular, they approached the pandas in the first decade, and stayed away in the second decade. This is mainly reflected in the weakened threats of nighttime light-represented human activities on the core large populations of the Qinling, Minshan and Qionglai Mountains from 2010 to 2020, but the threats on the peripheral populations continued to strengthen, where nighttime light became not only closer but also more intensified. The increase in nighttime light intensity and range were also significantly positively correlated with the increase in habitat fragmentation, especially during 2000–2010. Our study reveals the dynamic changes of nighttime light-represented human activities’ threats to wild giant panda and its habitat, gives advices for effectively protecting giant pandas and their habitats from human activities, and has implications for the assessment of human activities’ threats to other species in the world.

North Korea is one of the most restrictive countries in terms of data accessibility, especially at the regional level. This research assesses the regional economy in North Korea using the nighttime light data. It estimates GRDP per capita at the county level in 2012–2020, the Kim Jong-Un era, and assesses the inequality of North Korean regions. The results reveal that cities such as Sinuiju have a comparable level of GRDP per capita with Pyongyang and that the spatial distribution of low-income counties changes from regions near South Korean borders to regions near Chinese borders as sanctions are implemented. Also, it appears that substantial county-level inequality exists. It calls for a need for the county-level economic research of North Korea despite the lack of regional data.

Nighttime light data and land use data derived from remote sensing satellites are commonly used to monitor human activities on the Earth. A thorough understanding of the spatiotemporal interactions between these data enables better characterization and reconstruction of urbanization processes. In this work, a pixelwise fusion analysis using nighttime light data and land use data was performed to explore the spatiotemporal correlation between urban nighttime light luminosity transitions and land use changes. We found that the nighttime luminosity transitions are tightly related to the corresponding land use change types at the pixel level. A significant spatial correlation was identified between the nighttime light luminosity transitions and urban lands with intensive human activity. Relevant policies enacted in Beijing have greatly influenced the local light luminosity and land use type transitions. Pixels with change values in light luminosity in the same range have the same types of land use changes, the reasons for the changes in light luminosity are similar, and the spatial distribution characteristics are the same. This study provides a theoretical basis for quickly assessing changes in urban land use types through transitions in nighttime light luminosity. Simultaneously, the data in our study after integrating nighttime light luminosity and land use information perform well in urban development research and can provide valuable datasets and decision-making references for adjusting and optimizing urban sustainable development policies.

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R2: 0.93) and the city scale (R2: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales.

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.

Urbanization is a crucial indicator which reflects the socio-economic development of a country or region. The regions across the Taiwan Strait (TSR) have garnered attention worldwide as being representative of typical urbanization development along the southeastern coast. Currently, research in the TSR predominantly focuses on individual regions, with limited academic achievements comparing urbanization paths across the strait. In particular, the domain of comparative analysis of the spatiotemporal characteristics of urbanization dynamics in TSR by using long time series of nighttime light data remains largely underexplored. Therefore, this study focused on comparing the urbanization paths in the TSR and analyzing the spatiotemporal characteristics of urbanization by using the long-term nighttime light data from 1992 to 2020. Additionally, some methods such as Theil–Sen median trend analysis, Mann–Kendall significance test, Hurst exponent, spatial statistics, and time series were used to quantitatively analyze the spatial distribution patterns and temporal trends of nighttime lights in the TSR since 1992. The results were as follows: (1) From 1992 to 2020, the spatial distribution of nighttime light data in TSR exhibited significant spatial heterogeneity, with high-value areas mainly located in southeastern Fujian and northwestern Taiwan, while low-value areas were concentrated in Fujian’s inland regions; (2) During this period, nighttime lighting data increased from 729,863 in 1992 to 2,729,052 in 2020, and the percentage of its high-value (40–063) increased from 2.59% in 1992 to 12.22% in 2020; (3) Comparison of nighttime light data across representative cities from Taiwan (Taipei, Hsinchu) and Fujian (Xiamen, Fuzhou) uncovered distinct growth patterns—while Taiwanese cities had a high initial brightness value (the lowest value in the last 30 years was 518,379.4), their growth was relatively slow (average growth rate of 17%); Fujian cities, on the other hand, started from lower initial brightness value (the lowest value in the last 30 years was 35,123.1), but displayed substantial growth vigor (average growth rate of 222%); (4) During the study period, the nighttime light data of the vast majority of cities in the TSR demonstrated a significant increasing trend, particularly in coastal areas and urban centers; (5) Predictions of future trend variation suggest that the significantly increasing trend of cities surrounding Taiwan’s primary metropolitan areas will intensify, whereas metropolitan regions such as Keelung may witness a decline in future trend variations. However, only a mere 0.03% of the nighttime light data show a significant decreasing trend. Additionally, there are distinct differences in the urbanization development stages of the TSR. Fujian is currently undergoing rapid urbanization, while Taiwan’s urbanization has entered a stable stage. The study reveals that factors such as geographical location, natural resources, transportation infrastructure, population size, and industrial structure collectively influence the urbanization characteristics of the TSR. This research bears substantial significance for deepening the comprehension of the patterns and processes of urbanization development in the TSR and provides valuable insights for urban construction and development across the strait.

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

COVID-19 has caused almost 770,000 deaths in the United States by November 2021. The nighttime light (NTL), representing the intensity of human activities, may reflect the degree of human contacts and therefore the intensity of COVID-19 transmission. This study intended to assess the associations between NTL differences and COVID-19 incidence and mortality among U.S. counties. The COVID-19 data of U.S. counties as of 31 December 2020 were collected. The average NTL values for each county in 2019 and 2020 were derived from satellite data. A negative binomial mixed model was adopted to assess the relationships between NTL intensity and COVID-19 incidence and mortality. Compared to the counties with the lowest NTL level (0.14–0.37 nW/cm2/sr), those with the highest NTL level (1.78–59.61 nW/cm2/sr) were related with 15% higher mortality rates (mortality rate ratio:1.15, 95 %CI: 1.02–1.30, p-value: 0.02) and 23% higher incidence rates (incidence rate ratio:1.23, 95 %CI: 1.13–1.34, p-value

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler’s criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of −0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia’s total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management.

This study investigates whether the intensity of human activities conducted by urban populations and carried by urban land follows a wave-shaped diffusion rule using a harmonized DMSP-like NTL dataset during 1992–2018 in 234 cities of China. The results show that variations in the intensity of human activities are diffused in a wave-shaped manner from the urban center to the periphery in cities of different sizes and structures. The results demonstrate that variations in the intensity of human activity also exhibit a wave-shaped diffusion pattern, which is best modeled by a Gaussian function with an average R2 of 0.79 and standard deviation of 0.36 across all fitted functions. The outward movement of these waves in monocentric cities with an urban population

Accompanied by China’s rapid economic growth, significant urban greening has occurred in Chinese cities, in particular in the urban core areas. In contrast, rapid urbanization and economic growth also led to a high probability of vegetation degradation in urban fringe regions. However, these significant spatial differences in urban greenness associated with economic growth in Chinese cities are not well understood. This study explored the spatiotemporal characteristics of the nighttime light (NTL) and annual maximum enhanced vegetation index (EVImax) in urban areas from 2001 to 2020. A strong decoupling status between economic growth and urban greenness on the national scale was found. Overall, 49.15% of urban areas showed a decoupling status. Spatially, this percentage of urban areas with a decoupling status would significantly decrease when the long-term average NTL surpasses 51. Moreover, this significant threshold of decoupling status was found in 189 cities out of 344 (54.65%) in China. This threshold in each city showed significant spatial heterogeneity but can mostly be attributed to the gradient in the long-term average precipitation (Pmean) of each city during the period of 2001–2020. Specifically, a spatial increase in Pmean of 100 mm responded to a decrease in the threshold of 0.4 DN (p < 0.01). In contrast, there was no significant correlation between the threshold and the economic growth status of each city. Our results provide valuable insights for coordinating the development of urban greening and economic growth.

World cultural heritage refers to properties recognized as having historical, social, and anthropological value. Global urbanization has changed the land cover, land use, transportation, landscape, and local environment in cities, and thus exposed World Heritage sites to risks induced by direct or indirect damaging factors. In this paper, an urbanization intensity index (UII) was developed to quantitatively measure urban dynamics in the vicinity of World Heritage sites. This index is based on three global Earth observation datasets, including a global human settlement layer, a global population grid product, and a global nighttime light imagery. Large UII values represent high urbanization levels and intensive human activities in the study area and vice versa. The assessment results show that the mean UII value at 79 world cultural heritage sites in the Belt and Road region increased from 0.26 in 2000 to 0.29 in 2015. The heritage sites were then classified into four types based on the change rates of UIIs. A total of seven heritage sites were identified as exposed to risks due to urban sprawl and infrastructure expansion. The UII dataset can be combined with UNESCO’s periodic reports and site-specific data to provide valuable information for international communities to develop heritage preservation policies. The dataset is available at http://www.dx.doi.org/10.11922/sciencedb.980.

The impervious surface area (ISA) has become an important parameter in the areas such as surface runoff, climate change, and socioeconomic analysis. However, using remote sensing data to accurately map ISA is still difficult due to its complexity in the arid and semi-arid areas with large amounts of bare soil, sparse vegetation, and a small ISA. In this study, we have selected Ordos city as study area and examined the spatial pattern and dynamics in the years of 1990, 1995, 2000, 2005, 2010, 2015, and 2019 by using multiple data sources with a combination method. The results indicate that the ISAs in 2010, 2015, and 2019 were extracted with overall accuracies of 90.6%, 89.2%, and 91.8% with kappa coefficients of 0.79, 0.76, and 0.82, respectively. The ISA extraction results of the Urban Index (UI), Normalized Difference Built-Up Index (NDBI), Index-based Built-Up Index (IBI), and Impervious Built-up Index from optical sensors (IBUIopt) were used for comparison. Meanwhile, a correlation analysis between socioeconomic factors and the ISA of the typical resource-based city of Ordos was conducted. The spatial pattern and dynamics of the ISA data in Ordos city are of great significance to the study of socioeconomic and environmental changes and provide a reference to examine the effect of ISAs in other resource-based cities.