Why VIIRS data are superior to DMSP for mapping nighttime lights

For more than forty years the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has been the only satellite system collecting global low-light imaging data.  A series of twenty-four DMSP satellites have collected low-light imaging data.  The design of the OLS has not changed significantly since satellite F-4 flew in the late 1970’s and OLS data have relatively coarse spatial resolution, limited dynamic range, and lack in-flight calibration.  In 2011 NASA and NOAA launched the Suomi National Polar Partnership (SNPP) satellite carrying the first Visible Infrared Imaging Radiometer Suite (VIIRS)  instrument.  The VIIRS collects low light imaging data and has several improvements  over the OLS’ capabilities.  In this paper we contrast the nighttime low light imaging collection capabilities of these two systems and compare their data products

Using the Short-Wave Infrared for Nocturnal Detection of Combustion Sources in VIIRS Data

Night-time images from the SNPP satellite VIIRS scanning radiometer in visible and infrared spectral bands provide invaluable data for detection and characterization of natural and technological combustion sources on the surface of the Earth, such as forest fires, gas flares, steel mills or active volcanoes. The presence of sub-pixel hot infrared (IR) emission sources can be readily detected at night in 1.6 micron near-infrared M10 channel. Their temperature and radiant heat intensity can be estimated by fitting of the Planck black-body spectral curve to the observed radiances of VIIRS infrared M-channels out to 4 um. VIIRS instrument is sensitive to the IR sources over a wide range of temperatures. This method can discriminate low temperature sources such as volcanoes and forest fires from the high temperature gas flares with 300 m average location error. The processing includes correction for panoramic “bow-tie” effect and filtering of the false detections resulting from sensor bombardment by the cosmic rays, especially at the aurora rings and at the South Atlantic anomaly. False detections can be largely removed by correlating of the observed bright spots in M10 channel with other infrared and the visible day-night band. NGDC NOAA provides global daily detection products for thousands of IR sources as KMZ vector maps and as CSV tables

MAPPING THE ARTIFICIAL SKY BRIGHTNESS IN EUROPE FROM DMSP SATELLITE MEASUREMENTS: THE SITUATION OF THE NIGHT SKY IN ITALY IN THE LAST QUARTER OF CENTURY

ABSTRACT. We present a project to map the artificial sky brightness in Europe in the main astronomical photometrical bands with a resolution better than 3 km. The aim is to understand the state of night sky pollution in Europe, to quantify the present situation and to allow future monitoring of trends. The artificial sky brightness in each site at a given position on the sky is obtained by the integration of the contributions produced by every surface area in the surroundings of the site. Each contribution is computed taking in account the propagation in the atmosphere of the upward light flux emitted by the area and measured from DMSP satellites. The project is a long term study in which we plan to take in account successively of many different details in order to improve the maps. We present, as a preliminary result, a map of the V-band artificial sky brightness in Italy in 1998 and we compare it with the map obtained 27 years earlier by Bertiau, Treanor and De Graeve. Predictions for the artificial sky brightness within the next 27 years are also shown

MAPPING THE ARTIFICIAL SKY BRIGHTNESS IN EUROPE FROM DMSP SATELLITE MEASUREMENTS: THE SITUATION OF THE NIGHT SKY IN ITALY IN THE LAST QUARTER OF CENTURY

ABSTRACT. We present a project to map the artificial sky brightness in Europe in the main astronomical photometrical bands with a resolution better than 3 km. The aim is to understand the state of night sky pollution in Europe, to quantify the present situation and to allow future monitoring of trends. The artificial sky brightness in each site at a given position on the sky is obtained by the integration of the contributions produced by every surface area in the surroundings of the site. Each contribution is computed taking in account the propagation in the atmosphere of the upward light flux emitted by the area and measured from DMSP satellites. The project is a long term study in which we plan to take in account successively of many different details in order to improve the maps. We present, as a preliminary result, a map of the V-band artificial sky brightness in Italy in 1998 and we compare it with the map obtained 27 years earlier by Bertiau, Treanor and De Graeve. Predictions for the artificial sky brightness within the next 27 years are also shown

Spectral Identification of Lighting Type and Character

We investigated the optimal spectral bands for the identification of lighting types and the estimation of four major indices used to measure the efficiency or character of lighting. To accomplish these objectives we collected high-resolution emission spectra (350 to 2,500 nm) for forty-three different lamps, encompassing nine of the major types of lamps used worldwide. The narrow band emission spectra were used to simulate radiances in eight spectral bands including the human eye photoreceptor bands (photopic, scotopic, and “meltopic”) plus five spectral bands in the visible and near-infrared modeled on bands flown on the Landsat Thematic Mapper (TM). The high-resolution continuous spectra are superior to the broad band combinations for the identification of lighting type and are the standard for calculation of Luminous Efficacy of Radiation (LER), Correlated Color Temperature (CCT) and Color Rendering Index (CRI). Given the high cost that would be associated with building and flying a hyperspectral sensor with detection limits low enough to observe nighttime lights we conclude that it would be more feasible to fly an instrument with a limited number of broad spectral bands in the visible to near infrared. The best set of broad spectral bands among those tested is blue, green, red and NIR bands modeled on the band set flown on the Landsat Thematic Mapper. This set provides low errors on the identification of lighting types and reasonable estimates of LER and CCT when compared to the other broad band set tested. None of the broad band sets tested could make reasonable estimates of Luminous Efficacy (LE) or CRI. The photopic band proved useful for the estimation of LER. However, the three photoreceptor bands performed poorly in the identification of lighting types when compared to the bands modeled on the Landsat Thematic Mapper. Our conclusion is that it is feasible to identify lighting type and make reasonable estimates of LER and CCT using four or more spectral bands with minimal spectral overlap spanning the 0.4 to 1.0 um region

VIIRS Nightfire: Satellite Pyrometry at Night

The Nightfire algorithm detects and characterizes sub-pixel hot sources using multispectral data collected globally, each night, by the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The spectral bands utilized span visible, near-infrared (NIR), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The primary detection band is in the SWIR, centered at 1.6 μm. Without solar input, the SWIR spectral band records sensor noise, punctuated by high radiant emissions associated with gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Planck curve fitting of the hot source radiances yields temperature (K) and emission scaling factor (ESF). Additional calculations are done to estimate source size (m2), radiant heat intensity (W/m2), and radiant heat (MW). Use of the sensor noise limited M7, M8, and M10 spectral bands at night reduce scene background effects, which are widely reported for fire algorithms based on MWIR and long-wave infrared. High atmospheric transmissivity in the M10 spectral band reduces atmospheric effects on temperature and radiant heat retrievals. Nightfire retrieved temperature estimates for sub-pixel hot sources ranging from 600 to 6,000 K. An intercomparison study of biomass burning in Sumatra from June 2013 found Nightfire radiant heat (MW) to be highly correlated to Moderate Resolution Imaging Spectrometer (MODIS) Fire Radiative Power (MW)

DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration

The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable lights products are made using operational OLS data collected at high gain settings, resulting in sensor saturation on brightly lit areas, such as city centers. This has been a paramount shortcoming of the DMSP-OLS stable lights time series. This study outlines a methodology that greatly expands the dynamic range of the OLS data using observations made at different fixed-gain settings, and by incorporating the areas not affected by saturation from the stable lights product. The radiances for the fixed-gain data are computed based on each OLS sensor’s pre-flight calibration. The result is a product known as the OLS radiance calibrated nighttime lights. A total of eight global datasets have been produced, representing years from 1996 to 2010. To further facilitate the usefulness of these data for time-series analyses, corrections have been made to counter the sensitivity differences of the sensors, and coefficients are provided to adjust the datasets to allow inter-comparison

Extending the DMSP Nighttime Lights Time Series beyond 2013

Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights

Creating a Global Grid of Distributed Fossil Fuel CO2 Emissions from Nighttime Satellite Imagery

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