{"title":"Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning","authors":"Weixing Xu;Zhaocong Wu;Weihua Lin;Gang Xu","doi":"10.1109/JSTARS.2024.3454093","DOIUrl":null,"url":null,"abstract":"Global-scale remotely sensed nighttime light (NTL) data, such as the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) Day/Night Band (DNB) NTL data, has been widely applied across multiple disciplines. However, its broader application is still limited by its coarse spatial resolution. We proposed the NTL conditional multiscale downscaling model (NTL-CMDM) for downscaling NPP/VIIRS DNB. The model uses multisource scale factors as conditional constraints, progressively integrating NTL and scale factors to downscale NPP/VIIRS DNB from 500 to 130 m using data from 201 Chinese cities. The downscaled results were validated against the 130 m Loujia1-01 suggest that the NTL data quality was improved after downscaling, yielding higher the coefficient of determination (R: 0.407 versus 0.702) and lower root-mean-square error (RMSE: 7.020 versus 26.424 \n<italic>nWcm</i>\n<sup>−2</sup>\n<italic>sr</i>\n<sup>−1</sup>\n) values than those of the original NPP/VIIRS DNB. The downscaled results exhibit richer NTL feature details and show similarity to Luojia-1-01. More importantly, the downscaling enhances the accuracy of NTL statistical metrics, improving illuminated area by 10.23% and radiance estimation by 6.12%. Furthermore, the usability of the downscaled results was assessed by estimating county-level GDP. The GDP estimates based on the downscaled data were superior to those from the original NPP/VIIRS DNB data and consistent with the estimates obtained from Luojia1-01. Finally, generalization ability test using different algorithms in multiple cities demonstrate that NTL-CMDM is robust to cities with different NTL structures. The study verifies the practicability of employing deep learning methods to downscale NTL data, providing a feasible pathway for acquiring high-resolution NTL data over an expanded area.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663836","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663836/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Global-scale remotely sensed nighttime light (NTL) data, such as the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) Day/Night Band (DNB) NTL data, has been widely applied across multiple disciplines. However, its broader application is still limited by its coarse spatial resolution. We proposed the NTL conditional multiscale downscaling model (NTL-CMDM) for downscaling NPP/VIIRS DNB. The model uses multisource scale factors as conditional constraints, progressively integrating NTL and scale factors to downscale NPP/VIIRS DNB from 500 to 130 m using data from 201 Chinese cities. The downscaled results were validated against the 130 m Loujia1-01 suggest that the NTL data quality was improved after downscaling, yielding higher the coefficient of determination (R: 0.407 versus 0.702) and lower root-mean-square error (RMSE: 7.020 versus 26.424
nWcm
−2sr
−1
) values than those of the original NPP/VIIRS DNB. The downscaled results exhibit richer NTL feature details and show similarity to Luojia-1-01. More importantly, the downscaling enhances the accuracy of NTL statistical metrics, improving illuminated area by 10.23% and radiance estimation by 6.12%. Furthermore, the usability of the downscaled results was assessed by estimating county-level GDP. The GDP estimates based on the downscaled data were superior to those from the original NPP/VIIRS DNB data and consistent with the estimates obtained from Luojia1-01. Finally, generalization ability test using different algorithms in multiple cities demonstrate that NTL-CMDM is robust to cities with different NTL structures. The study verifies the practicability of employing deep learning methods to downscale NTL data, providing a feasible pathway for acquiring high-resolution NTL data over an expanded area.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.