# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 1 Introduction  
After a severe natural disaster, quick and effective assessment of the affected areas is vital for post-disaster response and rescue. Traditional methods of disaster assessment largely rely on field survey data [1; 2]. However, obtaining survey data is time-consuming and particularly difficult in isolated areas. Remote sensing data offer near real-time and large-scale information about the land surface and have been used widely in disaster assessment. Compared with optical and radar remote sensing data that are mainly used to identify ground deformation or structural damages due to natural disasters [3; 4; 5; 6], nighttime light (NTL) remote sensing data can directly reflect human activities becauseartificial lights from cities, towns, industrial sites, fishing boats, and other human activities are recorded at night [7; 8]. A natural disaster may damage buildings and infrastructure, cause power outages, and reduce human activities, all of which can cause changes in light, which serves as a basis for assessing the impact of natural disasters using NTL data. There are two commonly used NTL data sources: Defense Meteorological Satellite Program's Operational Line Scan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. DMSP-OLS has been acquiring nighttime light imagery since the early 1970s. However, its limitations include coarse radiometric accuracy, low spatial resolution, lack of onboard calibration, and limited dynamic range [9]. The new generation of NPP-VIIRS DNB data provides unprecedented nighttime light observation capability that overcomes some of the limitations in DMSP-OLS images [10], NPP-VIIRS DNB has a higher radiometric accuracy with onboard calibration and has a specified dynamic range of approximately seven orders of magnitude, from \(3\times 10^{-9}\) W\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) to 0.02 W\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\), which ensures the ability to detect extremely low-level light [11]. NPP-VIIRS DNB data provide global coverage with a 12-h revisit time (at about 1:30 a.m. and 1:30 p.m. local time) and with a spatial resolution of about 750 m throughout the scan. The yearly or monthly composite NTL data are being increasingly used as an indicator to estimate various socioeconomic indicators such as gross domestic product (GDP) [12; 13; 14], population [15; 16], poverty [17; 18], electric power consumption [19; 20; 21], carbon dioxide (CO\({}_{2}\)) emissions [22; 23], and others [24; 25; 26; 27], as well as to evaluate military conflicts [28; 29; 30] and to extract urban built-up areas [31; 32]. However, the composite NTL data are not suitable for immediate assessment of disasters due to their low temporal resolution. The application of the DMSP-OLS daily images to assessing disasters is limited by the cost of the data and its internal limitations [33]. The NPP-VIIRS DNB daily data are available free of charge, which enables its use in disaster assessment. Daily NTL data from both NPP-VIIRS DNB and DMSP-OLS have been used to detect power outages due to storms and to assess the damage by earthquakes. For example, [PERSON] et al. [34] overlaid the daily NTL image after Hurricane Fran with the reference data of stable lights from DMSP-OLS data to form a color composite image and visually identify areas with power outages; [PERSON] et al. [35] produced a false-color composite image of pre- and post-hurricane nighttime light from daily NPP-VIIRS DNB data to detect changes in light and identify areas with possible power outages. [PERSON] et al. [36] differentiated the pre- and post-hurricane nighttime light imagery based on the NPP-VIIRS DNB to produce a Percent of Normal Light (PNL) image and developed a neural network model to detect power outages. [PERSON] et al. [37] used a time-series mean NPP-VIIRS DNB radiance to estimate the light outage and [PERSON] et al.