Earth System Science Data | 2021
An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration
Abstract
Abstract. The nighttime light (NTL) satellite data have been widely\nused to investigate the urbanization process. The Defense Meteorological\nSatellite Program Operational Linescan System (DMSP-OLS) stable nighttime\nlight data and Suomi National Polar-orbiting Partnership Visible Infrared\nImaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely\nused NTL datasets. However, the difference in their spatial resolutions and\nsensor design requires a cross-sensor calibration of these two datasets for\nanalyzing a long-term urbanization process. Different from the traditional\ncross-sensor calibration of NTL data by converting NPP-VIIRS to\nDMSP-OLS-like NTL data, this study built an extended time series (2000–2018)\nof NPP-VIIRS-like NTL data through a new cross-sensor calibration from\nDMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL\ndata (2013–2018). The proposed cross-sensor calibration is unique due to the\nimage enhancement by using a vegetation index and an auto-encoder model.\nCompared with the annual composited NPP-VIIRS NTL data in 2012, our product\nof extended NPP-VIIRS-like NTL data shows a good consistency at the pixel\nand city levels with R2 of 0.87 and 0.95, respectively. We also found\nthat our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our\nextended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial\npattern and temporal consistency which are similar to the composited\nNPP-VIIRS NTL data. In addition, the resulting product could be easily\nupdated and provide a useful proxy to monitor the dynamics of demographic\nand socioeconomic activities for a longer time period compared to existing\nproducts. The extended time series (2000–2018) of nighttime light data is\nfreely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et\nal., 2020).