A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm²/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm²/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

Electric power services are fundamental to prosperity and economic development. Disruptions in the electricity power service can range from minutes to days. Such events are common in many developing economies, where the power generation and delivery infrastructure is often insufficient to meet demand and operational challenges. Yet, despite the large impacts, poor data availability has meant that relatively little is known about the spatial and temporal patterns of electric power reliability. Here, we explore the expressions of electric power instability recorded in temporal profiles of satellite observed surface lighting collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) low light imaging day/night band (DNB). The nightly temporal profiles span from 2012 through to mid-2020 and contain more than 3000 observations, each from a total of 16 test sites from Africa, Asia, and North America. We present our findings in terms of various novel indicators. The preprocessing steps included radiometric adjustments designed to reduce variance due to the view angle and lunar illumination differences. The residual variance after the radiometric adjustments suggests the presence of a previously unidentified source of variability in the DNB observations of surface lighting. We believe that the short dwell time of the DNB pixel collections results in the vast under-sampling of the alternating current lighting flicker cycles. We tested 12 separate indices and looked for evidence of power instability. The key characteristic of lights in cities with developing electric power services is that they are quite dim, typically 5 to 10 times dimmer for the same population level as in Organization for Economic Co-operation and Development (OECD) countries. In fact, the radiances for developing cities are just slightly above the detection limit, in the range of 1 to 10 nanowatts. The clearest indicator for power loss is the percent outage. Indicators for supply adequacy include the radiance per person and the percent of population with detectable lights. The best indicator for load-shedding is annual cycling, which was found in more than half of the grid cells in two Northern India cities. Cities with frequent upward or downward radiance spikes can have anomalously high levels of variance, skew, and kurtosis. A final observation is that, barring war or catastrophic events, the year-on-year changes in lighting are quite small. Most cities are either largely stable over time, or are gradually increasing in indices such as the mean, variance, and lift, indicating a trajectory that proceeds across multiple years.

The VIIRS day/night band (DNB) high gain stage (HGS) pixel effective dwell time is in the range of 2–3 milliseconds (ms), which is about one third of the flicker cycle present in lighting powered by alternating current. Thus, if flicker is present, it induces random fluctuations in nightly DNB radiances. This results in increased variance in DNB temporal profiles. A survey of flicker characteristics conducted with high-speed camera data collected on a wide range of individual luminaires found that the flicker is most pronounced in high-intensity discharge (HID) lamps, such as high- and low-pressure sodium and metal halides. Flicker is muted, but detectable, in incandescent luminaires. Modern light-emitting diodes (LEDs) and fluorescent lights are often nearly flicker-free, thanks to high-quality voltage smoothing. DNB pixel footprints are about half a square kilometer and can contain vast numbers of individual luminaires, some of which flicker, while others do not. If many of the flickering lights are drawing from a common AC supplier, the flicker can be synchronized and leave an imprint on the DNB temporal profile. In contrast, multiple power supplies will throw the flickering out of synchronization, resulting in a cacophony with less radiance fluctuation. The examination of DNB temporal profiles for locations before and after the conversion of high-intensity discharge (HID) to LED streetlight conversions shows a reduction in the index of dispersion, calculated by dividing the annual variance by the mean. There are a number of variables that contribute to radiance variations in the VIIRS DNB, including the view angle, cloud optical thickness, atmospheric variability, snow cover, lunar illuminance, and the compilation of temporal profiles using pixels whose footprints are not perfectly aligned. It makes sense to adjust the DNB radiance for as many of these extraneous effects as possible. However, none of these adjustments will reduce the radiance instability introduced by flicker. Because flicker is known to affect organisms, including humans, the development of methods to detect and rate the strength of flickering from space will open up new areas of research on the biologic impacts of artificial lighting. Over time, there is a trend towards the reduction of flicker in outdoor lighting through the replacement of HID with low-flicker LED sources. This study indicates that the effects of LED conversions on the brightness and steadiness of outdoor lighting can be analyzed with VIIRS DNB temporal profiles.

India is known to have unstable power supply, and many locations show an annual cycle in VIIRS Nighttime Light (VNL). In this study, autocorrelation function (ACF) analysis is used to identify the annual cycling in VNL. Two fundamentally different classification techniques are proposed to classify the ACF profile into one of the three arch types, i.e., acyclic, single peak, and dual peak. The results from the two classification techniques are closely compared to verify their output. This analysis is carried out for the entire territory of India in 15 arc second grid cells. The power stability data acquired from the India Human Development Survey (IHDS) and the Electricity Supply Monitoring Initiative (ESMI) are used to verify their relationship to the annual cycling of VNL. To further aide the analysis, land use/land class are accounted for by data from the India National Remote Sensing Center (NRSC). As a result, the contribution of power stability to VNL annual cycling in India is inconclusive due to the limitation of power stability data. Furthermore, other potential factors should be further examined.

Climate change induced extreme weather events will increase in intensity and frequency, leading to longer and widespread electricity outages. As an example, Winter Storm Uri in Texas left over 4.5 million customers without power between 14 and 18 February 2021. The social justice consequences of these events remain an outstanding question, as outage data are, typically, only available at the county level, obscuring detailed experiences. We produce a first-of-its-kind unique spatially resolved dataset of interruptions using satellite data on nighttime lights to track blackouts at the census block group (CBG) level. Correlating this dataset with demographic data reveals that minority CBGs were 1.5–3 times more likely to suffer from interruptions compared to predominantly white CBGs, whereas income status was positively correlated with the likelihood of interruption. The presence of critical facilities—including police and fire stations, hospitals, and water treatment facilities—in a CBG reduced the chances of interruptions by around 16 % , a small difference that does not otherwise explain the disparity among communities. We suggest explanations, test a subset of them, and propose further work needed to explain what drives these disparities.

The capability to map disasters and track recovery thereafter has seen substantial enhancement from the development of connected communication and imaging technology. In this work, we demonstrate a series of techniques for assessing the damage and the recovery process from the humanitarian crisis in Sana’a, Yemen. In particular, we make use of the Visible Infrared Imaging Radiometer (VIIRS) Day/Night Band (DNB), which produces nightly data on illumination levels globally at 750m resolution. While nearly all work that has previously been performed using the VIIRS DNB dataset uses monthly or annual composite data, we develop novel methods that leverage the nightly availability of this data source, particularly for mapping the scale and extents of illumination changes, accurately identifying the day when bombing occurred and illumination conditions changed, characterizing electricity outage patterns, and tracing the recovery of nighttime lighting both spatially and temporally. For each of these methods, we compare with publicly-available datasets to verify the accuracy of our observations. In total, these data, available only 1 day after an event, and the resulting analyses can be especially valuable for disaster response as well as targeting and tracking investments in recovery.