scholarly journals Review of "An extended time-series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration"

2020 ◽  
Author(s):  
Anonymous
2021 ◽  
Vol 13 (3) ◽  
pp. 889-906
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Chengshu Yang ◽  
Yuyu Zhou ◽  
Shenjun Yao ◽  
...  

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).


2020 ◽  
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Chengshu Yang ◽  
Yuyu Zhou ◽  
Xingjian Qian ◽  
...  

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).


Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Yuyu Zhou ◽  
Hongxing Liu ◽  
Chengshu Yang ◽  
...  

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0198189 ◽  
Author(s):  
Yao Yao ◽  
Dongsheng Chen ◽  
Le Chen ◽  
Huan Wang ◽  
Qingfeng Guan

2018 ◽  
Vol 10 (2) ◽  
pp. 47 ◽  
Author(s):  
Min Zhao ◽  
Weiming Cheng ◽  
Chenghu Zhou ◽  
Manchun Li ◽  
Kun Huang ◽  
...  

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