Abstract
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).
Highlights
With the artificial electric light widely equipped in most buildings and infrastructures, the nighttime light (NTL) remote sensing data have been extensively used to investigate human activities (Gaston et al, 2013; Falchi et al, 2011; Elvidge et al, 1997a; Baugh et al, 2013; Li et al, 2018)
We found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010
2017; Yu et al, 2014; Wu et al, 2019). While both of the two NTL datasets are acknowledged as good proxies for detecting the dynamics of demographic and socioeconomic activities at different spatial scales (Yang et al, 2019), their applications were always limited by their quality and available time span
Summary
With the artificial electric light widely equipped in most buildings and infrastructures, the nighttime light (NTL) remote sensing data have been extensively used to investigate human activities (Gaston et al, 2013; Falchi et al, 2011; Elvidge et al, 1997a; Baugh et al, 2013; Li et al, 2018). Chen et al, 2017; Yu et al, 2014; Wu et al, 2019) While both of the two NTL datasets are acknowledged as good proxies for detecting the dynamics of demographic and socioeconomic activities at different spatial scales (Yang et al, 2019), their applications were always limited by their quality and available time span. The DMSP-OLS NTL annually composited data can only be collected from 1992 to 2013 (Fig. 1) It has disadvantages, including the lack of on-orbit radiance calibration, saturation issues, and blooming issues (Letu et al, 2010; Cao et al, 2019; Elvidge et al, 2014; Levin et al, 2020), which limit its potential applications. An extended time series of nighttime light data with appropriate quality and a better consistency is desirable for long-term temporal nighttime light applications
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