1. Introduction
Human activities are continuously changing the Earth’s natural surface
and the urban systems, comprising a continuum of socioeconomic and
demographic phenomena. Monitoring global human footprint patterns is
crucial for understanding global environmental change, sustainability,
and socioeconomic status (Leu et al., 2008; Venter et al., 2016; Zhu et
al., 2020). Shifts in societies, cultures, economic system structures,
policies, technologies, and behaviors are rapidly affecting global
ecosystems (Malecki, 1997; Ojima et al., 1994; Steffen et al., 2006).
Human footprint expansion and reconstruction actions are driven by
social-ecological changes, such as population growth and the
consequential needs for natural resources. These drivers have modified
the long-term land cover and land use features over large areas of land
surfaces (Deng et al., 2009; Turner, 2010). Meanwhile, disturbance
stresses caused by social shocks and behavioral changes
(e.g.,
armed conflict and gathering events) engendered short-term changes on
the local scale (Baumann and Kuemmerle, 2016). Data-intensive frameworks
for monitoring human-induced land changes at large-scale have become
essential to enable a more timely, comprehensive, and deeper
understanding of human activity dynamics. This demand calls for the need
for reliable, timely, and large-area consistent information. In contrast
to available socioeconomic and field survey data, the remote sensing
nighttime light (NTL) imagery provides a reliable measure of
human
activity changes at the global scale with fine temporal and spatial
resolutions (Jensen and Cowen, 1999; Xie and Weng, 2016).
The remotely sensed NTL data provides the direct imprint of both the
spatial extent and emission intensity of the artificial light, which is
a good indicator of the human footprint changes (Elvidge et al., 1997;
Levin et al., 2020; Zhang and Seto, 2011). Characteristics of artificial
nocturnal illumination are often associated with the economic and
demographic structures of modern society (Green et al., 2015). The
artificial NTL intensity is strongly responded to the growth or decrease
of the health and development of the society (Hölker et al., 2010).
Strong correlations have been found among the
NTL
trends and socioeconomic status (Ma et al., 2012), which enables us to
estimate the spatial-temporal dynamics of society based on the NTL
changes. Accurate results have been produced by using the NTL datasets
as the major inputs for mapping the urbanization processes (Shi et al.,
2014), estimating Gross Domestic Product (GDP) and mapping poverty (Yu
et al., 2015), monitoring natural hazards and recurrent disaster impacts
on underserved communities (Machlis et al., 2022; Román et al., 2019),
armed conflicts (Li et al., 2018), cultural behaviors (Liu et al., 2019;
Román and Stokes, 2015), and detecting long-term landscape changes in
the urbanized regions (Chen et al., 2019).
Compared with the previous Defense Meteorological Satellite Program’s
Operational Line Scanner (DMSP/OLS) sensor, the Visible Infrared Imaging
Radiometer Suite (VIIRS) Day/Night Band (DNB) provides higher spatial
resolution NTL data with significant improvement in its quality,
traceability, and consistency (Elvidge et al., 2017). However, high
uncertainties caused by both the dynamics of the emission sources and
the environmental impacts still exist in the VIIRS DNB observations
(Coesfeld et al., 2018; Elvidge et al., 2022; Li et al., 2020; Wang et
al., 2021) which makes time series analysis (e.g., daily) of DNB
observations extremely challenging. Moreover, the VIIRS DNB observations
are inevitably subject to the extraneous impacts of the angular effects,
surface BRDF and albedo, lunar phases, atmospheric effects, cloud and
snow contamination, and vegetation canopy (Wang et al., 2021). To
alleviate the significant variation caused by the external effects,
previous studies used the monthly or annual composited NTL data to
smooth the variation (Elvidge et al., 2021; Levin, 2017; Liu et al.,
2019; Yang et al., 2020), which is a viable solution but also
significantly reduces the data temporal frequencies, making it difficult
to provide timely information and monitor short-term changes (Xie et
al., 2019; Zhao et al., 2020; Zheng et al., 2021).
NASA’s Lunar-BRDF-corrected Black Marble NTL product (VNP46A2) provides
daily 15-arc-second spatial resolution Visible Infrared Imaging
Radiometer Suite (VIIRS) Day/Night Band (DNB) data with operational
correction for the lunar phase effects (Román et al., 2018). Significant
reduction of the temporal variation has been achieved with the
correction of the major sources of noise from the lunar cycle (Elvidge
et al., 2022; Wang et al., 2021) which provides new opportunities for
analyzing NTL dynamics based on the daily DNB data for the first time.
However, there are still some remaining factors that could cause large
variations in NASA’s Black Marble products, such as cloud and snow
missed in the Quality Assurance (QA) flag (Wang et al., 2021),
vegetation phenology, surface albedo (Levin, 2017; Tang et al., 2021),
and angular effect from the illuminating artificial lights (Li et al.,
2022; Tan et al., 2022), which makes their direct usage for time series
analysis difficult.
In this study, we aimed to develop a new algorithm for continuous
monitoring of NTL changes based on daily VIIRS DNB observations from
NASA’s Black Marble standard product suite, which adds robustness to the
large variation caused by cloud and cloud shadow missed in the Level 3
QA flagging process, vegetation phenology, snow, and angular effects
introduced by illuminating artificial lights.