Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jag.2021.102513
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dc.titleA feasible framework to downscale NPP-VIIRS nighttime light imagery using multi-source spatial variables and geographically weighted regression
dc.contributor.authorYe, Yang
dc.contributor.authorHuang, Linyan
dc.contributor.authorZheng, Qiming
dc.contributor.authorLiang, Chenxin
dc.contributor.authorDong, Baiyu
dc.contributor.authorDeng, Jinsong
dc.contributor.authorHan, Xiuzhen
dc.date.accessioned2022-10-26T08:58:40Z
dc.date.available2022-10-26T08:58:40Z
dc.date.issued2021-12-01
dc.identifier.citationYe, Yang, Huang, Linyan, Zheng, Qiming, Liang, Chenxin, Dong, Baiyu, Deng, Jinsong, Han, Xiuzhen (2021-12-01). A feasible framework to downscale NPP-VIIRS nighttime light imagery using multi-source spatial variables and geographically weighted regression. International Journal of Applied Earth Observation and Geoinformation 104 : 102513. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jag.2021.102513
dc.identifier.issn0303-2434
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/233511
dc.description.abstractThe cloud-free monthly composite of global nighttime light (NTL) data of the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provides indispensable indications of human activities and settlements. However, the coarse spatial resolution (15 arc sec) of NTL imagery greatly restricts its application potential. This study proposes a feasible framework to downscale NPP-VIIRS NTL using muti-source spatial variables and geographically weighted regression (GWR) method. High-resolution auxiliary variables were acquired from the Landsat 8 OLI/ TIRS and social media platforms. GWR-based downscaling procedures were consequently implemented to obtain NTL at a 100-m resolution. The downscaled NTL data were validated against Loujia1-01 imagery based on the coefficient of determination (R2) and root-mean-square error (RMSE). The results suggest that the data quality was suitably improved after downscaling, yielding higher R2 (0.604 vs. 0.568) and lower RMSE (8.828 vs. 9.870 nW/cm2/sr) values than those of the original NTL data. Finally, the NTL was extendedly applied to detect impervious surfaces, and the downscaled NTL had higher accuracy than the original NTL. Therefore, this study facilitates data quality improvement of NPP-VIIRS NTL imagery by downscaling, thus enabling more accurate applications. © 2021 The Authors
dc.publisherElsevier B.V.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectDownscaling
dc.subjectGeographically weighted regression (GWR)
dc.subjectImpervious surface detection
dc.subjectNighttime light (NTL)
dc.typeArticle
dc.contributor.departmentDEPT OF BIOLOGICAL SCIENCES
dc.description.doi10.1016/j.jag.2021.102513
dc.description.sourcetitleInternational Journal of Applied Earth Observation and Geoinformation
dc.description.volume104
dc.description.page102513
dc.published.statePublished
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