Assessing the stability of surface lights for use in retrievals of nocturnal atmospheric parameters

Abstract

The detection and characterization of aerosols are inherently limited at night because the important information provided by visible spectrum observations is not available and infrared bands have limited sensitivity to aerosols. The VIIRS Day-Night Band (DNB) onboard the Suomi- NPP satellite is a first-of-its-kind calibrated sensor capable of collecting visible and near-infrared observations during both day and night. Multiple studies have suggested that anthropogenic light emissions such as those from cities and gas flares may be useable as light sources for the retrieval of atmospheric properties, including cloud and aerosol optical depth. However, their use in this capacity requires proper characterization of their intrinsic variation, which represents a source of retrieval uncertainty. In this study we use 18 months of cloud-cleared VIIRS data collected over five selected geographic domains to assess the stability of anthropogenic light emissions and their response to varied satellite and lunar geometries. Time series are developed for each location in each domain for DNB radiance, four infrared channels, and satellite and lunar geometric variables, and spatially resolved correlation coefficients are computed between DNB radiance and each of the other variables. This analysis finds that while many emissive light sources are too unstable to be used reliably for atmospheric retrievals, some sources exhibit a sufficient stability (relative standard deviation > 20 %). Additionally, we find that while the radiance variability of surrounding surfaces (i.e., unpopulated land and ocean) is largely dependent on lunar geometry, the anthropogenic light sources are more strongly correlated with satellite viewing geometry. Understanding the spatially resolved relationships between DNB radiance and other parameters is a necessary first step towards characterizing anthropogenic light emissions and establishes a framework for a model to describe variability in a more general sense.