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   %). 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.