1Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 35002, China
2The Academy of Digital China, Fuzhou University, Fuzhou 350002, China
3Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
4School of Geographic Sciences, East China Normal University, Shanghai 200241, China
5Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
1Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 35002, China
2The Academy of Digital China, Fuzhou University, Fuzhou 350002, China
3Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
4School of Geographic Sciences, East China Normal University, Shanghai 200241, China
5Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
Abstract. The nighttime light (NTL) satellite data have been widely used to investigate 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 of their spatial resolutions and sensor design makes it difficult to directly use these two datasets together for a long-term analysis of urbanization. To solve this issue, an extended time-series (2000–2018) of NPP-VIIRS-like NTL data were proposed in this study through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). 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 a good accuracy by comparing 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 a good 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 socio-economic activities for a longer time period compared to existing products. The extended time-series (2000–2018) of nighttime light data are freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).
An extended time-series (2000–2018) of NPP-VIIRS-like nighttime light (NTL) data were proposed through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data, our extended NPP-VIIRS-like NTL data has a high accuracy and also shows a good spatial pattern and temporal consistency. It could be a useful proxy to monitor the dynamics of urbanization for a longer time period compared to existing NTL data.
An extended time-series (2000–2018) of NPP-VIIRS-like nighttime light (NTL) data were proposed...
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