Day/Night Band Research Articles

Capturing the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime lights

This paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality.

Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM2.5 estimations. This study estimated nighttime PM2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM2.5 concentrations. Consequently, this study estimated nighttime PM2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) of 0.65–0.90, compared to MLR, with R2 of 0.15–0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5.

Urban nighttime aerosol optical depth retrieval methods from SDGSAT-1 glimmer data

ABSTRACT Aerosol Optical Depth (AOD) is vital for assessing air quality and climate. Nighttime lights (NTL) provide a new data source for urban AOD inversion, but current NTL data suffer from low spatial resolution, limiting detailed AOD studies in smaller urban areas and hindering high-precision AOD algorithms. This study introduces methods for retrieving AOD using Glimmer Imager (GLI) data from the newly launched SDGSAT-1. Three methods were proposed: the standard deviation method, look-up-table method, and collaborative inversion method with VIIRS Day Night Band (DNB) data. Applied to Beijing, these methods produced a 10 m resolution AOD product. The look-up-table method achieved the highest correlation (R = 0.8876) compared to the standard deviation (R = 0.8224) and collaborative inversion (R = 0.8322) methods. The look-up-table method performed best with ample data, while the collaborative inversion method proved more reliable with limited data. These findings demonstrate the effectiveness of SDGSAT-1 GLI data in accurately retrieving nighttime AOD, offering significant potential for monitoring urban pollution.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

Generation of monthly VIIRS nighttime lights time-series (1992–2013) images using deep learning (cGAN) technique

Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

Satellite Hyperspectral Nighttime Light Observation and Identification with DESIS

The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources.

Estimating population density using open-access satellite images and geographic information system: Case of Al Ain city, UAE

Efficient and real-time estimates of population density are crucial for monitoring urban systems and disaster response. To develop this capability, this study estimates population density using covariates derived from remote-sensing images [e.g., building density, vegetation temperature light population index (VTLPI), and day–night band (DNB)]. Data were obtained from open-access satellite images, specifically Sentinel 2A, Landsat 8, and NPP-VIIRS images. Building density, VTLPI, and DNB were extracted at the district level and compared with population density. Furthermore, vector data from geographic information systems, such as human points of interest and road networks, were used to develop a geographically weighted regression model for estimating population density. Building density and DNB correlated positively with population density (r = 0.89), and population density correlated positively with the VTLPI (r = 0.75). Although all covariates correlated positively with estimates of population density, building density and the DNB showed the best correlation. Furthermore, both parameters were considerably related to human points of interest and road network density. Finally, using geographically weighted regression (GWR) and multiple linear regression (MLR) with the building density, DNB, and human points of interest as input, we predicted the population density. The GWR model built from independent variables correlates well with the population density (AICc = 526; R2 adj. = 0.89). Overall, the GWR and MLR produce similar results. To assess the transferability of the GWR model used for Al Ain, it was tested on Abu Dhabi, and the results produce an accuracy of 36%. This low accuracy may be attributed to the acquisition dates of the census data being different from those of the human points of interest. Further research is required to address such limitations in the proposed method and to provide additional support for using open-access remote-sensing images to estimate population density.

Fast Calculation on Atmospheric Rayleigh Scattering Extinction Optical Depth for Day/Night Band Radiative Transfer Model With Periodic Lunar Illumination

AbstractRayleigh scattering effect inspired by tiny atmospheric molecules generates significant extinction at relatively short wavelength, such as Day/Night Band (DNB) covering visible (VIS, 0.4–0.76 μm) and near‐infrared (NIR, 0.76–1.0 μm) wavelengths. This salient extinction effect coupled with periodic changes in lunar illumination and wide DNB will further lead to increased complexity in nocturnal fast radiative transfer model (RTM) calculation of DNB. For these reasons, this study is aimed at developing a precise and robust fast calculation method on Rayleigh scattering extinction optical depth (RayOD) for pertinent fast RTM calculation of DNB. Firstly, we coupled the lunar spectral irradiance model and the parameterized Rayleigh scattering extinction calculation module into a RTM for forward simulations. Moreover, we find that the errors in DNB‐averaged RayOD calculated from the classical fast algorithm from RTTOV (Radiative Transfer for TOVS) of training the DNB instrument coefficients (about 20% of relative error in reflectance) and the parameterization method using the central wavelength of DNB (about 5%) are too large to be tolerable in fast RTM. Finally, to address this issue, we skillfully conceptualize a new equivalent optimal central wavelength algorithm to retrieve a dynamically wavelength in DNB using observation scattering and lunar phase angles for rapidly calculating DNB‐averaged RayOD. The relative errors in simulated reflectance from coupled RTM aforementioned are subtle nuances, which are about 10−5∼10−6 order of magnitude. Besides, this robust algorithm can also correct the relative error of about 1% at dark surface caused by periodic changes in lunar illumination.

Outdoor artificial light at night and cardiovascular disease in adults: a Chinese nationwide cohort study

Abstract Background Although the electric light is a significant promotion in the human progress and civilization, its adverse effects of outdoor artificial light at night (ALAN) are being extensively studied. However, the association of outdoor ALAN and cardiovascular disease (CVD) has never been confirmed in large nationwide studies, especially in urban areas with high exposure levels and in rural areas with low exposure levels, respectively. Purpose To explore the association between outdoor ALAN and CVD. Methods This national study enrolled 12,686 adults ≥ 40 years across China. The outdoor ALAN exposure (nW/cm2/sr) with the resolution of the 500 meters around each participant's location was obtained from satellite image data via Visible Infrared Imaging Radiometer Suite day-night band on board the Joint Polarorbiting Satellite System. CVD was defined by a doctor's record of diagnoses, including heart disease and stroke. Poisson regression model and restricted cubic spline models were used to estimate the relationship between outdoor ALAN and CVD in urban and rural areas, respectively. Subgroup analysis was also conducted using Poisson models for each subgroup. Results At a median follow-up of 7 years, 1,732 cases of CVD were identified. The mean age of the participants was 58 years and 49% were males. In urban areas, outdoor ALAN exposure at the highest quartile (Q4) increased the incidence of CVD among participants by 34% compared to those exposure at the lowest quartile (Q1) (incident rate ratio, IRR: 1.34,95% CI: 1.07—1.69, p = 0.01), adjusting for age, sex, BMI, education, smoke ever, alcohol consumption, physical activity and household consumption. In rural areas, compared with those at the Q1 of outdoor ALAN exposure, exposure at the Q4 reduced the incidence of CVD by 17% (IRR: 0.83, 95% CI: 0.70—0.98, p = 0.028) after full adjustment. Dose-response relationship indicated that outdoor ALAN levels were linearly associated with decreased CVD in rural area (p for non-linearity = 0.370), while the association between outdoor ALAN levels and CVD was shown as an inverse "U" shape in urban area (p for non-linearity ≤ 0.001). Subgroup analysis shown that the CVD risk of middle-aged people, people with history of alcohol consumption, or people with low levels of physical activity were susceptible to outdoor ALAN exposure. Conclusions Outdoor ALAN exposure was associated with the risk of CVD, which was heterogeneous with different levels of exposure range. In rural areas with low outdoor ALAN exposure settings, outdoor ALAN exposure was negatively correlated with the risk of developed CVD, while the association was reverse in urban areas with high outdoor ALAN exposure settings. We call for a reduction in the use of high-intensity ALAN and to pay attention to the effects of emotional, psychological, and biological changes associated with outdoor ALAN exposure on cardiovascular health.Figure 1

Calibrating Nighttime Satellite Imagery with Red Photometer Networks

The data retrieved from satellite imagery and ground-based photometers are the two main sources of information on light pollution and are thus the two main tools for tackling the problem of artificial light pollution at night (ALAN). While satellite data offer high spatial coverage, on the other hand, photometric data provide information with a higher degree of temporal resolution. Thus, studying the proper correlation between both sources will allow us to calibrate and integrate them to obtain data with both high temporal resolution and spatial coverage. For this purpose, more than 15,000 satellite measurements and 400,000 measurements from 72 photometers for the year 2022 were used. The photometers used were the Sky-Glow Wireless Autonomous Sensor (SG-WAS) and Telescope Encoder and Sky Sensor WIFI (TESS-W) types, located at different ground-based locations, mainly in Spain. These photometers have a spectral sensitivity closer to that of VIIRS than to the Sky Quality Meter (SQM). In this study, a good correlation of data from the Day–Night Band (DNB) from the Visible Infrared Imaging Radiometer Suite (VIIRS) with a red photometric network between 19.41 mag/arcsec2 and 21.12 mag/arcsec2 was obtained.

Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources

Aerosol Optical Depth (AOD) is a critical optical parameter that quantifies the degree of light attenuation by aerosols and serves as a fundamental indicator of atmospheric quality. Therefore, accurate quantification and retrieval of AOD is crucial for relevant studies. However, current satellite-based AOD retrieval algorithms suffer from inapplicability under low-light conditions, limiting the development of nighttime AOD retrieval. Under this context, we proposed a novel algorithm, namely Simultaneous Consideration of Artificial and Natural light Sources (SCANS), to obtain nighttime AOD. The core of the SCANS algorithm is considering the synergy of both the natural and artificial light sources to obtain nighttime AOD by integrating atmospheric radiative transfer simulation into an extinction method and performing multiple iterations. SCANS was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) and the retrieved nighttime AOD was validated with in-situ measurements from five AERONET sites. Results indicate that the Mean Bias Errors (MBEs) of the retrieved nighttime AOD range from 0.0 to 0.08 and the corresponding Root Mean Square Errors (RMSEs) range from 0.11 to 0.17, which exhibit better accuracy than that of the nighttime MERRA-2 AOD. We further compared the retrieved nighttime AOD with the corresponding Air Quality Index (AQI) measurements at six environment monitoring stations and obtained high correlation coefficients (i.e., ranging from 0.733 to 0.940), indicating SCANS's reliability and high accuracy. The proposed SCANS algorithm can effectively obtain nighttime AOD with high quality, thereby advancing research on the diurnal variation of crucial Earth's key elements.

Discovering Hidden Offshore Lighting Structures with Multiyear Low-Light Imaging Satellite Data

The NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has a capability to detect lighting present at the earth’s surface. This low-light imaging capability was built to satisfy a requirement from meteorologists to detect clouds at night in the visible based on reflected moonlight. On land, human settlements are the dominant source of DNB detected surface lighting. Lights are also detected offshore primarily arising from fishing boats using lights to attract catch. In 2015, the Earth Observation Group developed the VIIRS Boat Detection (VBD) product and today several fishery agencies use VBD data to monitor fishing activity and compliance with closures. Recently EOG compiled the full record VBD detections as a 15 arc second global grid. The record spans 2012-2021 in Asia and 2017-2021 elsewhere. Upon reviewing the multiyear accumulation of VBD detections we were surprised to find a diversity of previously unseen lighting features [1]. These structures are not evident in VBD from single nights but are only revealed by accumulating the detections across a year or more. The additional features include lit platforms, transit lanes, and vessel anchorages associated with ports and passage straits. Another surprising finding is geopolitical divisions in the density of cumulative vessel detections. In this paper we provide a look into one of the densest areas of VBD detections in the world, from the Yellow Sea, East China Sea and Sea of Japan (East Sea).

Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China

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.

Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea

Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

Sensitivity studies of nighttime top-of-atmosphere radiances from artificial light sources using a 3-D radiative transfer model for nighttime aerosol retrievals

Abstract. By accounting for surface-based light source emissions and top-of-atmosphere (TOA) downward lunar fluxes, we adapted the spherical harmonics discrete ordinate method (SHDOM) 3-dimensional (3-D) radiative transfer model (RTM) to simulate nighttime 3-D TOA radiances as observed from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi-NPP satellite platform. Used previously for daytime 3-D applications, these new SHDOM enhancements allow for the study of the impacts of various observing conditions and aerosol properties on simulated VIIRS-DNB TOA radiances. Observations over Dakar, Senegal, selected for its bright city lights and a large range of aerosol optical depth (AOD), were investigated for potential applications and opportunities for using observed radiances containing VIIRS-DNB “bright pixels” from artificial light sources to conduct aerosol retrievals. We found that using the standard deviation (SD) of such bright pixels provided a more stable quantity for nighttime AOD retrievals than direct retrievals from TOA radiances. Further, both the mean TOA radiance and SD of TOA radiances over artificial sources are significantly impacted by satellite viewing angles. Light domes, the enhanced radiances adjacent to artificial light sources, are strong functions of aerosol properties and especially aerosol vertical distribution, which may be further utilized for retrieving aerosol layer height in future studies. Through inter-comparison with both day- and nighttime Aerosol Robotic Network (AERONET) data, the feasibility of retrieving nighttime AODs using 3-D RTM SHDOM over artificial light sources was demonstrated. Our study shows strong potential for using artificial light sources for nighttime AOD retrievals, while also highlighting larger uncertainties in quantifying surface light source emissions. This study underscores the need for surface light emission source characterizations as a key boundary condition, which is a complex task that requires enhanced input data and further research. We demonstrate how quality-controlled nighttime light data from the NASA's Black Marble product suite could serve as a primary input into estimations of surface light source emissions for nighttime aerosol retrievals.

Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with Moonlight Remote Sensing: Systematic Evaluation in Svalbard

Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for under-standing and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. Traditional optical and Synthetic Aperture Radar (SAR) remote sensing still have limitations in the long-time series observation of polar regions. Although several studies have demonstrated the potential of moonlight remote sensing for mapping polar snow/ice covers, systematic evaluation on applying moonlight remote sensing to monitoring spatiotemporal dynamics of polar snow/ice covers, especially during polar night periods is highly demanded. Here we present a systematic assessment in Svalbard, Norway and using data taken from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) Day/Night Band (DNB) sensor to monitor the spatiotemporal dynamics of snow/ice covers during dark Arctic winters when no solar illumination available for months. We successfully revealed the spatiotemporal dynamics of snow/ice covers from 2012 to 2022 during polar night/winter periods, using the VIIRS/DNB time series data and the object-oriented Random Forests (RF) algorithm, achieving the average accuracy and kappa coefficient of 96.27% and 0.93, respectively. Our findings indicate that the polar snow/ice covers show seasonal and inter-seasonal dynamics, thus requiring more frequent observations. Our results confirm and realize the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and together with other types of remote sensing data, moonlight remote sensing will be a very useful tool for polar studies and climate change.

Estimation of Ground-Level PM2.5 Concentration at Night in Beijing-Tianjin-Hebei Region with NPP/VIIRS Day/Night Band

Reliable measures of nighttime atmospheric fine particulate matter (PM2.5) concentrations are essential for monitoring their continuous diurnal variation. Here, we proposed a night PM2.5 concentration estimation (NightPMES) model based on the random forest model. This model integrates the radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), moon phase angle, and meteorological data. We collected 13486 samples from the Beijing Tianjin–Hebei (BTH) region. The determination coefficient (R2) of the NightPMES model was 0.82, the root mean square error (RMSE) was 16.67 µg/m3, and the mean absolute error (MAE) was 10.20 µg/m3. The applicability analysis of the moon phase angles indicated that the amount of data available increased by 60% while the accuracy remained relatively unchanged. In the seasonal model, the meteorological factors and DNB radiance were found to be the primary factors affecting the PM2.5 concentration in different seasons. In conclusion, this study provided a method for estimating nighttime PM2.5 concentration that will improve our understanding of air pollution and associated trends in PM2.5 variation.

A Review of the Far-Reaching Usage of Low-Light Nighttime Data

To assess the current and future utility of low-light satellite data, this paper reviewed 1630 papers, presentations, theses, and dissertations using day/night band (DNB) data from the visible infrared imaging radiometer suite (VIIRS) imager and its precursor, the Defense Meteorological Satellite Program’s Operational Linescan system (DMSP-OLS) series from the 1970s through to the year 2021. By the way of a categorical system, we take inventory of the myriad applications of these data to a wide variety of disciplines, ranging from social to natural science, oceans to atmosphere, and biology to civil engineering. Papers from social science fields dominate this spectrum, pointing to the unique aspect of low-light observations in their ability to observe aspects of human civilization at night. We also look at the stratification between applications using natural vs. artificial light, the use of moonlight, and the context of the key earth climate system elements. In light of these findings, a discussion is provided for the future of low-light measurements. Since the start of the VIIRS series, there has been a rapid increase in interest in the use of these data for numerous fields, pointing towards a nascent field centered on the nocturnal earth system science that is enabled by these novel and newly quantifiable measurements. This study is of significant importance in evaluating current uses of low-light data and possible architecture solutions for next-generation satellites.

Aurora Detection From Nighttime Lights for Earth and Space Science Applications

AbstractThis research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora‐contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite‐ or ground‐based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar‐orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source.