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Wilson, T., & Xiong, X. (2020). Intercomparison of the SNPP and NOAA-20 VIIRS DNB High-Gain Stage Using Observations of Bright Stars. IEEE Transactions on Geoscience and Remote Sensing, , 1–8.
Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecrafts is a multispectral Earth-observing instrument with bands covering wavelengths from visible to long-wave infrared. Among these bands is a panchromatic day/night band (DNB) with a broad spectral response ranging from 500 to 900 nm, and a high dynamic range spanning over seven orders of magnitude, allowing for observations to take place during both daytime and nighttime. The DNB operates at three gain levels, with low- and mid-gain stages and two high-gain stages (HGSs). The HGS is capable of detecting dim city lights during Earth-view observations at night as well as bright stars through the instrument space-view port. Since SNPP and N20 are at opposite points of the same orbit, each VIIRS instrument is able to observe the same stars with the DNB in successive orbits. This will allow us to make a direct comparison of the relative calibration of each instrument using stars over a range of spectral classes. In this article, we develop methodology for accurately identifying target stars in order to make proper comparisons between the DNB HGS of each instrument. We then take observations from multiple stars in order to compute the ratio in the measured irradiance for each instrument as a function of spectral class. For K-type stars, which have the least spectral change over the DNB wavelength range, we measure a calibration bias between the SNPP and N20 DNB HGS of approximately 4%, which is stable over the duration of the N20 mission.
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Schwarting, T., McIntire, J., Oudrari, H., & Xiong, X. (2019). JPSS-1/NOAA-20 VIIRS Day-Night Band Prelaunch Radiometric Calibration and Performance. IEEE Transactions on Geoscience and Remote Sensing, , 1–13.
Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the first Joint Polar-Orbiting Satellite System series 1 (JPSS-1) has a panchromatic, three gain stage, day-night band (DNB) capable of imaging the Earth under illumination conditions ranging from reflected moonlight to daytime scenes. The DNB has four charged-coupled devices (CCDs) with 32 different modes of time-delay integration and subpixel aggregation to achieve high SNR in low light conditions while maintaining roughly constant spatial resolution across scan. During the prelaunch testing phase, these 32 different aggregation modes are separately calibrated over a large dynamic range (covering seven orders of magnitude) through a series of radiometric tests designed to generate initial calibration coefficients for the sensor data record (SDR) operational algorithm, assess radiometric performance, and determine compliance with the sensor design requirements. Early in the environmental testing at the Raytheon El Segundo facility, nonlinear behavior was discovered in some DNB edge of scan aggregation modes at low signal levels. In response to this nonlinearity, the test program was altered to characterize the radiometric performance both in the baseline configuration and with a modified aggregation scheme that eliminates the modes used at the end of scan, replacing them with an unaffected adjacent mode and trading off spatial resolution for improved linearity. Presented in this paper is the radiometric performance under both sensor configurations including dynamic range, sensitivity, radiometric uncertainty, and nonlinearity along with a discussion of the potential impact to DNB on-orbit calibration and SDR performance.
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Warrant, E. (2016). Superior vision in nocturnal insects inspires new night vision technologies. SPIE Newsroom.
Abstract: Algorithms that dramatically improve the quality of video sequences captured in very dim light have been developed on the basis of the neural mechanisms in nocturnal insects with excellent visual capabilities.
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de Meester, J., & Storch, T. (2020). Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas. Sensors (Basel), 20(11).
Abstract: Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.
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Stone, J. E., Phillips, A. J. K., Ftouni, S., Magee, M., Howard, M., Lockley, S. W., et al. (2019). Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions. Sci Rep, 9(1), 11001.
Abstract: A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy individuals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule; (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule; and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within +/- 1 hour in 67% and +/- 1.5 hours in 100% of participants, with mean absolute error of 41 +/- 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within +/- 1 hour in 66% and +/- 2 hours in 87% of participants, with mean absolute error of 63 +/- 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within +/- 1 hour in 42% and +/- 2 hours in 53% of participants, with mean absolute error of 143 +/- 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within +/- 2 hours for the vast majority of individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.
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