Abstract. The nighttime light (NTL) satellite data have been widely
used to investigate the urbanization process. The Defense Meteorological
Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime
light data and Suomi National Polar-orbiting Partnership Visible Infrared
Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely
used NTL datasets. However, the difference in their spatial resolutions and
sensor design requires a cross-sensor calibration of these two datasets for
analyzing a long-term urbanization process. Different from the traditional
cross-sensor calibration of NTL data by converting NPP-VIIRS to
DMSP-OLS-like NTL data, this study built an extended time series (2000–2018)
of NPP-VIIRS-like NTL data through a new cross-sensor calibration from
DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL
data (2013–2018). The proposed cross-sensor calibration is unique due to the
image enhancement by using a vegetation index and an auto-encoder model.
Compared with the annual composited NPP-VIIRS NTL data in 2012, our product
of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel
and city levels with R2 of 0.87 and 0.95, respectively. We also found
that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our
extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial
pattern and temporal consistency which are similar to the composited
NPP-VIIRS NTL data. In addition, the resulting product could be easily
updated and provide a useful proxy to monitor the dynamics of demographic
and socioeconomic activities for a longer time period compared to existing
products. The extended time series (2000–2018) of nighttime light data is
freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et
al., 2020).
An easily implemented method to estimate impervious surface area on a large scale from MODIS time-series and improved DMSP-OLS nighttime light data
