TY - JOUR
T1 - A monthly night-time light composite dataset of NOAA-20 in China
T2 - a multi-scale comparison with S-NPP
AU - Hong, Yuchen
AU - Wu, Bin
AU - Song, Zhichao
AU - Li, Yangguang
AU - Wu, Qiusheng
AU - Chen, Zuoqi
AU - Liu, Shaoyang
AU - Yang, Chengshu
AU - Wu, Jianping
AU - Yu, Bailang
N1 - Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - Night-Time light (NTL) data have been widely used for monitoring the dynamics of human activities and socioeconomics. As a new-generation satellite for acquiring NTL data, the National Oceanic and Atmospheric Administration-20 (NOAA-20) was successfully launched in November 2017. To support broad-scale environmental applications, it is necessary to generate monthly NTL composites of NOAA-20. Taking China as the study area, we produced NOAA-20 monthly NTL composites from April 2019 to December 2019. First, we performed a series of de-noising steps to eliminate NOAA-20 NTL pixels affected by sunlight, moonlight, high scan angles, clouds and further eliminated NTL outliers in time series by using a modified z-score method. Then, we aggregated daily NOAA-20 NTL data to produce monthly NTL composites to improve data coverage and stability. Subsequently, we examined the consistency of monthly composites between NOAA-20 and S-NPP at multiple scales, including provincial, prefectural, and pixel levels. Our results show that the monthly NTL composites of NOAA-20 are in good agreement with that of S-NPP with an R2 ranging from 0.81 to 0.99. Besides, we found that the spatial variation trends of the NOAA-20 monthly NTL composites are similar to that of S-NPP monthly NTL composites. We believe that the monthly NTL composites of NOAA-20 are very close to that of S-NPP and will open up more opportunities for relevant NTL studies.
AB - Night-Time light (NTL) data have been widely used for monitoring the dynamics of human activities and socioeconomics. As a new-generation satellite for acquiring NTL data, the National Oceanic and Atmospheric Administration-20 (NOAA-20) was successfully launched in November 2017. To support broad-scale environmental applications, it is necessary to generate monthly NTL composites of NOAA-20. Taking China as the study area, we produced NOAA-20 monthly NTL composites from April 2019 to December 2019. First, we performed a series of de-noising steps to eliminate NOAA-20 NTL pixels affected by sunlight, moonlight, high scan angles, clouds and further eliminated NTL outliers in time series by using a modified z-score method. Then, we aggregated daily NOAA-20 NTL data to produce monthly NTL composites to improve data coverage and stability. Subsequently, we examined the consistency of monthly composites between NOAA-20 and S-NPP at multiple scales, including provincial, prefectural, and pixel levels. Our results show that the monthly NTL composites of NOAA-20 are in good agreement with that of S-NPP with an R2 ranging from 0.81 to 0.99. Besides, we found that the spatial variation trends of the NOAA-20 monthly NTL composites are similar to that of S-NPP monthly NTL composites. We believe that the monthly NTL composites of NOAA-20 are very close to that of S-NPP and will open up more opportunities for relevant NTL studies.
UR - https://www.scopus.com/pages/publications/85115236518
U2 - 10.1080/01431161.2021.1969057
DO - 10.1080/01431161.2021.1969057
M3 - 文章
AN - SCOPUS:85115236518
SN - 0143-1161
VL - 42
SP - 7931
EP - 7951
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 20
ER -