TY - JOUR
T1 - Evaluating the Ability of NOAA-20 Monthly Composite Data for Socioeconomic Indicators Estimation and Urban Area Extraction
AU - Li, Yangguang
AU - Song, Zhichao
AU - Wu, Bin
AU - Yu, Bailang
AU - Wu, Qiusheng
AU - Hong, Yuchen
AU - Liu, Shaoyang
AU - Wu, Jianping
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS' day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination R2 between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar R2 values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.
AB - The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS' day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination R2 between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar R2 values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.
KW - Electric power consumption (EPC)
KW - National Oceanic and Atmospheric Administration-20 Satellite (NOAA-20)
KW - National Polar Orbit Partnership-visible infrared imaging radiometer suite (NPP-VIIRS)
KW - gross domestic product (GDP)
KW - urban area extraction
UR - https://www.scopus.com/pages/publications/85124747632
U2 - 10.1109/JSTARS.2022.3149028
DO - 10.1109/JSTARS.2022.3149028
M3 - 文章
AN - SCOPUS:85124747632
SN - 1939-1404
VL - 15
SP - 1837
EP - 1845
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ER -