Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is published and openly available under the terms of the World Bank’s open data license.

The Defense Meteorological Program (DMSP) Operational Line-Scan System (OLS) has a unique capability to detect visible and near-infrared (VNIR) emission sources at night. Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data, two composites were produced. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.

The India Lights platform shows light output at night for 20 years for 600,000 villages across India. The Defense Meteorological Satellite Program (DMSP) has taken pictures of the Earth every night from 1993 to 2013. Researchers at the University of Michigan, in collaboration with the World Bank, used the DMSP images to extract the data you see on the India Lights platform. Each point you see on the map represents the light output of a specific village at a specific point in time. On the district level, the map also allows you to filter to view villages that have participated in India’s flagship electrification program. This tremendous trove of data can be used to look at changes in light output, which can be used to complement research about electrification in the country. About the Data: The DMSP raster images have a resolution of 30 arc-seconds, equal to roughly 1 square kilometer at the equator. Each pixel of the image is assigned a number on a relative scale from 0 to 63, with 0 indicating no light output and 63 indicating the highest level of output. This number is relative and may change depending on the gain settings of the satellite’s sensor, which constantly adjusts to current conditions as it takes pictures throughout the day and at night. Methodology To derive a single measurement, the light output values were extracted from the raster image for each date for the pixels that correspond to each village's approximate latitude and longitude coordinates. We then processed the data through a series of filtering and aggregation steps. First, we filtered out data with too much cloud cover and solar glare, according to recommendations from the National Oceanic and Atmospheric Administration (NOAA). We aggregated the resulting 4.4 billion data points by taking the median measurement for each village over the course of a month. We adjusted for differences among satellites using a multiple regression on year and satellite to isolate the effect of each satellite. To analyze data on the state and district level, we also determined the median village light output within each administrative boundary for each month in the twenty-year time span. These monthly aggregates for each village, district, and state are the data that we have made accessible through the API. To generate the map and light curve visualizations that are presented on this site, we performed some additional data processing. For the light curves, we used a rolling average to smooth out the noise due to wide fluctuations inherent in satellite measurements. For the map, we took a random sample of 10% of the villages, stratified over districts to ensure good coverage across regions of varying village density. Acknowledgments The India Lights project is a collaboration between Development Seed, The World Bank, and Dr. Brian Min at the University of Michigan. •Satellite base map © Mapbox. •India village locations derived from India VillageMap © 2011-2015 ML Infomap. •India population data and district boundaries © 2011-2015 ML Infomap. •Data for reference map of Uttar Pradesh, India, from Natural Earth Data •Banerjee, Sudeshna Ghosh; Barnes, Douglas; Singh, Bipul; Mayer, Kristy; Samad, Hussain. 2014. Power for all : electricity access challenge in India. A World Bank study. Washington, DC ; World Bank Group. •Hsu, Feng-Chi, Kimberly Baugh, Tilottama Ghosh, Mikhail Zhizhin, and Christopher Elvidge. "DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration." Remote Sensing 7.2 (2015): 1855-876. Web. •Min, Brian. Monitoring Rural Electrification by Satellite. Tech. World Bank, 30 Dec. 2014. Web. •Min, Brian. Power and the Vote: Elections and Electricity in the Developing World. New York and Cambridge: Cambridge University Press. 2015. •Min, Brian, and Kwawu Mensan Gaba. Tracking Electrification in Vietnam Using Nighttime Lights. Remote Sensing 6.10 (2014): 9511-529. •Min, Brian, and Kwawu Mensan Gaba, Ousmane Fall Sarr, Alassane Agalassou. Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery. International Journal of Remote Sensing 34.22 (2013):8118-8141. Disclaimer Country borders or names do not necessarily reflect the World Bank Group's official position. The map is for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

The Day Night Band (DNB), from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar-orbiting Satellite System (JPSS) satellites, provides global daily measurements of nocturnal visible and near-infrared light that are suitable for Earth system science and applications studies. The VIIRS Nighttime Lights Monthly Cloud-Free Composite is produced using average radiance composite images and excludes any data impacted by stray light. There are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to cloud-cover, especially in the tropical regions, or due to solar illumination, as happens toward the poles in their respective summer months. Therefore, when used for analysis, it is imperative that users of these data utilize the cloud-free observations file (band-2) and not assume a value of zero in the average radiance image (band-1) means that no lights were observed.Geographic CoverageGlobal from 75N to 65SCoverage is affected by the length of day during different times of the year. For example, summer time in the northern hemisphere will have less nighttime coverage due to longer days.Temporal CoverageMonthly from January 2014 - February 2024BandsBand-1: Monthly average radianceUnits: (avg_rade9h) nW/cm2/srBand-2: Cloud free observations per monthUnits: DaysCoordinate Reference SystemSource images are stored in Geographic WGS84 (EPSG:4326) and transformed on-the-fly to Web Mercator (EPSG:3857)Spatial Resolution15 arc second (~500m at the Equator)VIIRS Nighttime Lights product generation is credited to the Earth Observation Group, Payne Institute for Public Policy.

The Earth Observations Group (EOG) at National Oceanic and Atmospheric Administration (NOAA)/National Geophysical Data Center (NGDC) is producing a version 1 suite of average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). Prior to averaging, the DNB data is filtered to exclude data impacted by stray light, lightning, lunar illumination, and cloud-cover. Cloud-cover is determined using the VIIRS Cloud Mask product (VCM). In addition, data near the edges of the swath are not included in the composites (aggregation zones 29-32). Temporal averaging is done on a monthly and annual basis. The version 1 series of monthly composites has not been filtered to screen out lights from aurora, fires, boats, and other temporal lights. However, the annual composites have layers with additional separation, removing temporal lights and background (non-light) values. The version 1 products span the globe from 75N latitude to 65S. The products are produced in 15 arc-second geographic grids and are made available in geotiff format as a set of 6 tiles. The tiles are cut at the equator and each span 120 degrees of latitude. Each tile is actually a set of images containing average radiance values and numbers of available observations. The dataset is the night light annual composite in year of 2015. The dataset is a KML file which requires the Google earth to visualize. For other monthly and annual basis night light geotiff datasets (up to Sep 2017), please download at https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html#NTL_2015 Citation: the Earth Observation Group, NOAA National Geophysical Data Center

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor that provides global daily measurements of nocturnal visible and near-infrared (NIR) light that are suitable for Earth system science and applications. The VIIRS DNB's ultra-sensitivity in lowlight conditions enables us to generate …

Nighttime satellite imagery were accessed via Google Earth Engine). Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Several attributes are included - we used stable_lights which represents lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. The background noise was identified and replaced with values of zero.These data were provided to Google Earth Engine by teh National Centers for Environmental Information - National Oceanic and Atmospheric Administration of the United States (see Supporting Documentation).CANUE staff exported the annual data and extracted values of annual mean nighttime brightness for all postal codes in Canada for each year from 1992 to 2013 (DMTI Spatial, 2015).

This dataset contains key characteristics about the data described in the Data Descriptor A harmonized global nighttime light dataset 1992-2018. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Cloud-free coverages (cf_cvg) tally the total number of observations that went into each 30 arc second grid cell. This image can be used to identify areas with low numbers of observations where the quality is reduced. In some years there are areas with zero cloud-free observations in certain locations. This composite set is a component of the DMSP-OLS nighttime lights imagery for the years 1992-2013. The individual images are cloud-free composites made using all the available archived DMSP-OLS (Defense Meteorological Satellite Program - Operational Linescan System) smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each composite set is named with the satellite and the year (F121995 is from DMSP satellite number F12 for the year 1995).

Visualization OverviewThis visualization represents a "false color" band combination (Red = DNB, Green = DNB, Blue = Inverted M15) of data collected by the VIIRS instrument on the joint NASA/NOAA Suomi-NPP satellite. The imagery is most useful for identifying nighttime lights from cities, fires, boats, and other phenomena. At its highest resolution, this visualization represents the underlying data scaled to a resolution of 500m per pixel at the equator.The algorithm to combine the VIIRS DNB and M15 bands into an RGB composite was originally designed by the Naval Research Lab and was subsequently incorporated into NASA research and applications efforts. As you will see, nighttime city lights appear in shades of yellow, while clouds appear in shades of blue to yellow/white as the illumination from the moon changes over the lunar month. Hence, this visualization is colloquially referred to as a "blue-yellow RGB."

The following guidelines will aid in understanding this visualization.

Interpretation of both the presence and relative brightness of the city lights will be affected by the lunar cycle. This composite offers a qualitative assessment of the light conditions and should not be used as the sole source of information concerning power outages. During bright moonlight conditions, moonlight reflected from cloud tops and the land surface may also provide a yellow hue to those features. Comparisons of cloud-free conditions before and after a period of significant change, such as new city growth, disasters, fires, or other factors, may exhibit a change in emitted light (yellows) from those features over time. Multi-Spectral BandsAt its highest resolution, this visualization represents the underlying data scaled from its native 750m per pixel resolution to 500m per pixel at the equator. The following table lists the VIIRS bands that are utilized to create this visualization. See here for a full description of all VIIRS bands.BandDescriptionWavelength (µm)Resolution (m)DNBVisible (reflective)0.5 - 0.9750DNBVisible (reflective)0.5 - 0.9750M15 (Inverted)Longwave IR10.26 - 11.26750Temporal CoverageBy default, this layer will display the imagery currently available for today’s date. This imagery is a "daily composite" that is assembled from hundreds of individual data files. When viewing imagery for “today,” you may notice that only a portion of the map has imagery. This is because the visualization is continually updated as the satellite collects more data. To view imagery over time, you can update the layer properties to enable time animation and configure time settings. Currently, this layer is available from present back to April 30th, 2021. In the coming months, this will be extended to the start of the mission (October 28th, 2011).Data AccessThis visualization is generated from hourly and daily Near-Real Time versions of the "VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night" (VNP46A1_NRT) data product distributed by the Land, Atmosphere Near real-time Capability for EOS (LANCE). A standard quality version of the data product (VNP46A1), which is distributed by the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (LAADS DAAC), is also available within 1-2 days of acquisition. You may use the Earthdata Search client to search for near real-time and science quality data files and associated documentation and services. Additionally, you may use the Worldview Snapshots tool to download custom images in a GeoTIFF , JPEG, PNG, or KMZ format for offline use.NASA Global Imagery Browse Services (GIBS), NASA Worldview, & NASA LANCEThis visualization is provided through the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery for hundreds of NASA Earth science datasets and science parameters. Through its services, and the NASA Worldview client, GIBS enables interactive exploration of NASA's Earth imagery for a broad range of users. The data and imagery are generated within 3 hours of acquisition through the NASA LANCE capability.Esri and NASA Collaborative ServicesThis visualization is made available through an ArcGIS image service hosted on Esri servers and facilitates access to a NASA GIBS service endpoint. For each image service request, the Esri server issues multiple requests to the GIBS service, processes and assembles the responses, and returns a proper mosaic image to the user. Processing occurs on-the-fly for each and every request to ensure that any update to the GIBS imagery is immediately available to the user. As such, availability of this visualization is dependent on both the Esri and the NASA GIBS services.

The Defense Meteorological Program (DMSP) Operational Line-Scan System (OLS) has a unique capability to detect visible and near-infrared (VNIR) emission sources at night. This collection contains global nighttime lights images with no sensor saturation. The sensor is typically operated at a high-gain setting to enable the detection of moonlit clouds. However, with six bit quantization and limited dynamic range, the recorded data are saturated in the bright cores of urban centers. A limited set of observations at low lunar illumination were obtained where the gain of the detector was set significantly lower than its typical operational setting (sometimes by a factor of 100). Sparse data acquired at low-gain settings were combined with the operational data acquired at high-gain settings to produce the set of global nighttime lights images with no sensor saturation. Data from different satellites were merged and blended into the final product in order to gain maximum coverage. For more information, see this readme file from the provider. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.

DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.

We used a series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).

The version 1 products span the globe from 75N latitude to 65S. The products are produced in 1000m resolution and are made available in GeoTIFF format. Each year has two scenes.

Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". These fields are followed by a filename extension. The fields are described below using this example filename:

CCNL_1992_1_1.0

Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)

Field 2: year "1992"

Field 3: first scene “1”

Field 4: version “1.0”

The Consistent and Corrected Nighttime Lights (CCNL) dataset is a reprocessed version of the Defense Meteorological Program (DMSP) Operational Line-Scan System (OLS) Version 4. A series of methods was used to mitigate the impact of inter-annual inconsistency, saturation, and blooming effects and to improve data quality. CCNL Version 1 spans the globe from 75N to 65S and has 1 km pixel size.

Visualization OverviewThis visualization represents a "false color" band combination (Red = DNB, Green = DNB, Blue = Inverted M15) of data collected by the VIIRS instrument on the joint NASA/NOAA Suomi-NPP satellite. The imagery is most useful for identifying nighttime lights from cities, fires, boats, and other phenomena. At its highest resolution, this visualization represents the underlying data scaled to a resolution of 500m per pixel at the equator.The algorithm to combine the VIIRS DNB and M15 bands into an RGB composite was originally designed by the Naval Research Lab and was subsequently incorporated into NASA research and applications efforts. As you will see, nighttime city lights appear in shades of yellow, while clouds appear in shades of blue to yellow/white as the illumination from the moon changes over the lunar month. Hence, this visualization is colloquially referred to as a "blue-yellow RGB."The following guidelines will aid in understanding this visualization.Interpretation of both the presence and relative brightness of the city lights will be affected by the lunar cycle. This composite offers a qualitative assessment of the light conditions and should not be used as the sole source of information concerning power outages. During bright moonlight conditions, moonlight reflected from cloud tops and the land surface may also provide a yellow hue to those features. Comparisons of cloud-free conditions before and after a period of significant change, such as new city growth, disasters, fires, or other factors, may exhibit a change in emitted light (yellows) from those features over time.Multi-Spectral BandsAt its highest resolution, this visualization represents the underlying data scaled from its native 750m per pixel resolution to 500m per pixel at the equator. The following table lists the VIIRS bands that are utilized to create this visualization. See here for a full description of all VIIRS bands.BandDescriptionWavelength (µm)Resolution (m)DNBVisible (reflective)0.5 - 0.9750DNBVisible (reflective)0.5 - 0.9750M15 (Inverted)Longwave IR10.26 - 11.26750Temporal CoverageBy default, this layer will display the imagery currently available for today’s date. This imagery is a "daily composite" that is assembled from hundreds of individual data files. When viewing imagery for “today,” you may notice that only a portion of the map has imagery. This is because the visualization is continually updated as the satellite collects more data. To view imagery over time, you can update the layer properties to enable time animation and configure time settings. Currently, this layer is available from present back to April 30th, 2021. In the coming months, this will be extended to the start of the mission (October 28th, 2011).Data AccessThis visualization is generated from hourly and daily Near-Real Time versions of the "VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night" (VNP46A1_NRT) data product distributed by the Land, Atmosphere Near real-time Capability for EOS (LANCE). A standard quality version of the data product (VNP46A1), which is distributed by the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (LAADS DAAC), is also available within 1-2 days of acquisition. You may use the Earthdata Search client to search for near real-time and science quality data files and associated documentation and services. Additionally, you may use the Worldview Snapshots tool to download custom images in a GeoTIFF , JPEG, PNG, or KMZ format for offline use.NASA Global Imagery Browse Services (GIBS), NASA Worldview, & NASA LANCEThis visualization is provided through the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery for hundreds of NASA Earth science datasets and science parameters. Through its services, and the NASA Worldview client, GIBS enables interactive exploration of NASA's Earth imagery for a broad range of users. The data and imagery are generated within 3 hours of acquisition through the NASA LANCE capability.Esri and NASA Collaborative ServicesThis visualization is made available through an ArcGIS image service hosted on Esri servers and facilitates access to a NASA GIBS service endpoint. For each image service request, the Esri server issues multiple requests to the GIBS service, processes and assembles the responses, and returns a proper mosaic image to the user. Processing occurs on-the-fly for each and every request to ensure that any update to the GIBS imagery is immediately available to the user. As such, availability of this visualization is dependent on both the Esri and the NASA GIBS services.

The row image (avg_vis) contains the average of the visible band digital number values with no further filtering. Data values range from 0-63. This composite set is a component of the DMSP-OLS nighttime lights imagery for the years 1992-2013. The individual images are cloud-free composites made using all the available archived DMSP-OLS (Defense Meteorological Satellite Program - Operational Linescan System) smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each composite set is named with the satellite and the year (F121995 is from DMSP satellite number F12 for the year 1995). Five ArcGIS image services are available, each containing images from 1992-2013.

Urban extents were extracted using nighttime lights data in 1996 and 2010. Full documentation can be found in the link

VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This yearly Lunar BRDF-Adjusted NTL collection record starts from January 1st 2012.

This dataset is intended for researchers, students, and policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Monthly nighttime lights (NTL) can clearly depict an area's prevailing intra-year socio-economic dynamics. 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 onwards). In the current study, an attempt has been made to generate synthetic monthly VIIRS-like products of 1992-2012, 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 using low and high thresholds. Improved DMSP and corrected VIIRS images from April 2012 - December 2013 are used in a conditional generative adversarial network (cGAN) along with Land Use Land Cover, as auxiliary input, to generate VIIRS-like imagery from 1992-2012. The modelled imagery was aggregated annually and showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products of 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 for also performed on both national and state scales. Visual analysis over 1992-2013 confirms a gradual increase in the brightness of the lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, a synthetically generated monthly VIIRS-like product is delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.