National Polar-orbiting Partnership Research Articles

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

Development of an Algorithm for Assessing the Scope of Large Forest Fire Using VIIRS-Based Data and Machine Learning

Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system.

Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China

In recent years, a substantial body of research has demonstrated the close relationship between the built environment and urban nighttime vitality. While scholars have established slightly varied evaluation systems for the built environment, most have utilized the open-source nighttime light (NTL) data (2012 ∼ ) from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite to characterize nighttime vitality. However, with the release of the high spatial resolution NTL data from Glimmer Imager for Urbanization (GIU) sensor on the Sustainable Development Goals Satellite 1 (SDGSAT-1) by the Chinese Academy of Sciences (CAS), more options are now available for indicating nighttime vitality. Yet, no studies have so far measured the representational capability of this new NTL data. In this study, we selected Shanghai, one of the most urbanized cities in Asia, as our research area. We utilized both GIU NTL data and VIIRS NTL data to represent nighttime vitality and collected multidimensional geospatial big data as indicators of the built environment. Various regression models were employed to explore the differences in explanatory power when using different NTL data. To obtain more reliable conclusions, we have also divided the study area according to urban functional zone categories and conducted experiments on both the overall scale (entire study area) and local scale (different urban functional zones). The results showed that regression models using GIU NTL data had higher R2 values both at overall scale(0.529 > 0.268) and local scale (0.704 > 0.641, 0.701 > 0.408, 0.537 > 0.512, 0.674 > 0.589), indicating stronger explanatory power of GIU NTL data. Furthermore, we found that the use of GIU NTL data revealed an amplification effect, where the impact of certain representative built environment factors (such as road density, building height, and vegetation coverage) on nighttime vitality was magnified, demonstrating more pronounced spatial heterogeneity. Our study investigated the performance of the new open-source SDGSAT1/GIU NTL data in representing nighttime vitality and shows its potential application in research on the relationship between the built environment and nighttime vitality. Additionally, our study also reveals the differences in the impact of the built environment on nighttime vitality across different urban functional zones. All these results will provide more valuable references for city managers aspiring to enhance the nighttime vitality and help them to make more rational planning plans.

Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations

Abstract. Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure but is limited by poor vertical resolution (> 2 km) and precipitation. In this study, we combine these two technologies in an optimal estimation approach, 1D variation method (1DVar), to improve the characterization of the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a radiative transfer model (RTM) that takes into account atmospheric absorption, as well as surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from large eddy simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the lower troposphere while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP (National Polar-orbiting Partnership) measurements.

Urban resilience assessment and its spatial correlation from the multidimensional perspective: A case study of four provinces in North-South Seismic Belt, China

Modern cities are facing increasingly complex challenges, and assessing urban resilience is crucial to improve their ability to withstand various types of shocks and disasters, especially in the China's North-South Seismic Belt with frequent tectonic activities and natural disasters. To address this issue, this study determined the weights of the assessment system based on the entropy weight method, and evaluated the urban resilience of four provinces in China's North-South Seismic Belt in multiple dimensions from the perspectives of society, economy, infrastructure, and ecology. Furthermore, we systematically explored the spatiotemporal trends of urban resilience and its spatial correlation from 2011 to 2021. Lastly, the assessment results were validated and analyzed by four historical earthquake cases using national polar-orbiting partnership - visible infrared imaging radiometer suite (NPP-VIIRS) nighttime light data. The findings reveal that the average urban resilience index increased from 0.027 to 0.058 from 2011 to 2021, signifying a remarkable surge of 115.42%. Sichuan emerged as a consistent frontrunner in terms of urban resilience. Overall, the study area shows a spatial distribution pattern of higher urban resilience in the east and lower resilience in the west, due to the larger population and more developed economy in the east. However, the south has a higher average annual growth rate, while the north has a lower average annual growth rate. A notable observation is the gradual reduction in the coefficient of variation of urban resilience from 0.823 in 2011 to 0.751 in 2021, indicating a decline in the disparity of resilience levels. Meanwhile, Moran's I gradually increased from 0.2017 in 2011 to 0.4476 in 2021, signifying a progressive intensification in the spatial correlation of urban resilience and an evident propensity toward aggregation. By selecting shifts in the total nighttime light index (TNLI) during representative earthquake incidents as an indirect gauge of urban resilience levels, this study finds congruence with the assessment outcomes, thereby further substantiating the precision of the urban resilience evaluations. Policymakers should prioritize the allocation of resources to less resilient cities to improve their ability to withstand and recover from disaster events. The urban resilience assessment system in this study, despite its multidimensionality, may still not be able to fully cover all the factors affecting urban resilience. This study provides a significant decision-making basis for policymakers in urban development and disaster response, as well as a useful reference for building a more robust and sustainable urban future.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Multiscale spatial-temporal evolution of energy carbon footprint in the Yellow River Basin of China based on DMSP/OLS and NPP/VIIRS integrated data.

Increased CO2 emissions from urban energy consumption pose a significant challenge to regional carbon mitigation policies. In this paper, we integrated two nighttime light (NTL) data: the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) composite data to estimate the energy carbon emissions from 2000 to 2019. Then the spatiotemporal dynamics of carbon footprint and deficit in the Yellow River Basin were analyzed at the provincial, municipal, and county scales combined with NPP data. The study shows that (1) the total amount of energy consumption CO2 emissions in the Yellow River Basin had increased from 1332 Mt in 2000 to 6469 Mt in 2019, but the average annual growth rate slowed down after 2010 from 11.5 to 5.61%. (2) From 2000 to 2018, the provinces with the highest carbon footprint and carbon deficit were concentrated in Inner Mongolia and Shanxi. In 2018, Inner Mongolia's carbon footprint was 1366.91 × 104 km2, accounting for 22.8% of the total. Cities with high carbon footprint were mainly economic centers and energy-intensive areas of various provinces. High-carbon deficit counties were mainly distributed in the western region. In 2018, 954 counties exhibited carbon deficits. (3) The carbon footprint in the Yellow River Basin at the municipal and county scales have a significant spatial correlation. The H-H clusters of the carbon footprint on the municipal scale were distributed in the middle reaches of the Yellow River Basin. At the county scale, the L-L clusters were mainly in Sichuan and eastern Henan regions. Through the analysis of the spatial and temporal evolution of carbon footprint and carbon deficit in the Yellow River Basin, it is significant to measure the degree of comprehensive coordination of carbon sources and sinks in the basin, to grasp the differences in the level of regional carbon emissions, and to promote synergistic regional governance, assist in the formulation of more precise carbon emission reduction policies, and to promote green and high-quality development.

Rasterizing CO2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013–2019

Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.

Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data

Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September–December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R2) of 0.81 and a mean absolute error (MAE) of 7.85 μg·m−3. Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.

Enterprise version 8 total column ozone algorithm (EV8TOz) development and applications on multiple sensors

ABSTRACT We expand the Version 8 Total Column Ozone algorithm (V8TOz) into an Enterprise V8TOz (EV8TOz), which provides a unique platform accommodating a variety of UV/Visible satellite measurements for total ozone (TOz) estimation. A satellite-oriented flexible dynamic input data space applicable to any existing and upcoming satellite UV/Visible sensor measurements is developed in EV8TOz. The EV8TOz also provides flexible choices of band adjustment for either conducting sensor original single band or combined broad band retrievals, and includes options for introducing soft calibration adjustments. The applications of EV8TOz to the Ozone Mapping and Profiler Suite (OMPS) aboard Suomi National Polar-orbiting Partnership (NPP) and NOAA-20 (N20), Global Ozone Monitoring Experiment-2 (GOME-2) aboard Meteorological Operational satellite (Metop)-B and MetOp-C and the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor (S5P) are demonstrated. As far as we know, it is the first time TROPOMI ozone has been estimated and soft-calibrated with this limited wavelength channels and table lookup technique for the V8TOz. Statistical analyses show that EV8TOz estimated global TOz with measurements from these five sensors on the same day have less than±2% differences relative to the NASA NPP retrievals.

Exploring the Correlations Between SNPP-VIIRS Nighttime Light Data and Population From a Multiple Scale Perspective

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 R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values 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.

Sliding Window Detection and Analysis Method of Night-Time Light Remote Sensing Time Series—A Case Study of the Torch Festival in Yunnan Province, China

The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership’s visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities’ NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world.

Macro- and Micro-physical Characteristics of Different Parts of Mixed Convective-stratiform Clouds and Differences in Their Responses to Seeding

This study investigates the cloud macro- and micro-physical characteristics in the convective and stratiform regions and their different responses to the seeding for mixed convective-stratiform clouds that occurred in Shandong province on 21 May 2018, based on the observations from the aircraft, the Suomi National Polar-Orbiting Partnership (NPP) satellite, and the high-resolution Himawari-8 (H8) satellite. The aircraft observations show that convection was deeper and radar echoes were significantly enhanced with higher tops in response to seeding in the convective region. This is linked with the conversion of supercooled liquid droplets to ice crystals with released latent heat, resulting in strengthened updrafts, enhanced radar echoes, higher cloud tops, and more and larger precipitation particles. In contrast, in the stratiform cloud region, after the Silver Iodide (AgI) seeding, the radar echoes become significantly weaker at heights close to the seeding layer, with the echo tops lowered by 1.4–1.7 km. In addition, a hollow structure appears at the height of 6.2–7.8 km with a depth of about 1.6 km and a diameter of about 5.5 km, and features such as icing seeding tracks appear. These suggest that the transformation between droplets and ice particles was accelerated by the seeding in the stratiform part. The NPP and H8 satellites also show that convective activity was stronger in the convective region after seeding; while in the stratiform region, a cloud seeding track with a width of 1–3 km appears 10 km downstream of the seeding layer 15 minutes after the AgI seeding, which moves along the wind direction as width increases.

Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.

Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data

Exploring the effect of urban spatial development pattern (UPD) on carbon dioxide emissions (CDEs) (EUC) is important for understanding low-carbon sustainable development. Numerous studies on EUC have mainly focused on individual cities or regions within the mixed conclusions due to the lack of reliable UPD indices and reasonable methods. Thus, taking China's 257 prefecture-level cities as experimental objects, a novel system approach was developed from the perspective of socioeconomic density distribution (SED) index to measure UPD on the basis of the Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data. EUC was then analyzed on the basis of the dynamic panel data model from multiple perspectives. Results show that the SED index can effectively measure UPD with rich spatial information from multiple dimensions. The coefficients of SED and (SED)2 are 0.129 and − 1.240, respectively, indicating that EUC shows a clear inverted U-shaped curve in China, i.e., an increase in UPD compactness increases CDEs at the beginning, and when a certain height is reached, an increase in UPD compactness decreases CDEs. Heterogeneity analysis indicates a U-shaped curve of EUC is found in megalopolis, and inverse U-shaped curve are observed in medium and small cities. Bus passenger volume, energy consumption, infrastructure, and housing demand are proven as the transmission factors of EUC. It is suggested that utilizing the positive externality effect of agglomeration and accelerating the inflection point of the inverse U-shaped curve may be necessary because the improvement of urban socioeconomic agglomeration will improve the UPD compactness and reduce CDEs.

DEEP LEARNING-BASED METHOD TO EXTEND THE TIME SERIES OF GLOBAL ANNUAL VIIRS-LIKE NIGHTTIME LIGHT DATA

Abstract. The nighttime light (NTL) remote sensed imagery has been applied in monitoring human activities from many perspectives. As the two most widely used NTL satellites, the Defense Meteorological Satellite Program (DMSP) Operational Linescan System and the Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) have different spatial and radiometric resolutions. Thus, some long-time series analysis cannot be conducted without effective and accurate cross-calibration of these two datasets. In this study, we proposed a deep-learning based model to simulate VIIRS-liked DMSP NTL data by integrating the enhanced vegetation index (EVI) data product from MODIS. By evaluating the spatial pattern of the results, the modified Self-Supervised Sparse-to-Dense networks delivered satisfying results of spatial resolution downscaling. The quantitative analysing of the simulated VIIRS-liked DMSP NTL with original VIIRS NTL showed a good consistency at the pixel level of four selected sub datasets with R2 ranging from 0.64 to 0.76, and RMSE ranging from 3.96-9.55. Our method presents that the deep learning model can learn from relatively raw data instead of fine processed data based on expert knowledge to cross-sensor calibration and simulation NTL data.

Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.

Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.

Remotely sensed nighttime lights reveal China's urbanization process restricted by haze pollution

Under the background of China's active formulation and implementation of haze control policies, clarifying the relationship between the urbanization process (UP) and haze pollution (HP) is an important prerequisite for urban sustainable development. Previous studies have demonstrated the effect of the urbanization process on haze pollution (EUH) from different perspectives, but less attention has been given to the effect of haze pollution on the urbanization process (EHU). Thus, taking 247 prefecture-level cities in China as experimental subjects, this study has attempted to analyze whether HP has an effect on China's UP based on the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) remotely sensed nighttime light data and econometric models. The results show that for every unit of HP increase, UP slows down by approximately 0.275 units, indicating that HP has restricted UP in China to a certain extent. In addition, in the eastern region of China, HP has also significantly restricted UP. However, in the central and western regions of China, EHU is not significant. Human capital (HC) has been proven to be an important transmission factor for EHU in large cities, medium-sized cities, and eastern developed areas. Based on the above analysis, the study proposed that haze control policies should be implemented according to the UP's characteristics in different regions, and the government should reasonably guide the flow of HC in different regions and promote coordinated development among regions.

Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road

The ambiguous relationship between the international economy, energy, and carbon emissions has become a significant factor restricting sustainable development. This study attempts to reveal the spatiotemporal dynamics and interrelationships of gross domestic product (GDP), electric power consumption (EPC), and carbon emissions (CE) in the Belt and Road (B&R) regions using Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing data. Annual nighttime light (ANL) images were synthesized, and the accuracy of the ANL data was improved using the multiperiod mask denoising method. Then, the GDP, EPC, and CE at 0.5 km resolution were modelled. Finally, the spatiotemporal dynamics of regional development were comprehensively analysed at multiple scales. The results reveal that the development in the eastern and western parts of the B&R regions has significant differences. China, India, and some countries in Southeast Asia have developed rapidly and in a relatively balanced manner, whereas the development of Central and Eastern Europe and western Russia has been relatively slow and uncoordinated. The geographical centre of the overall development of the B&R continued to migrate to the southeast. This study provides more detailed and comprehensive insights into GDP, EPC, and CE in the B&R regions.