Jinyu Guo , Feng Zhang , Hang Zhao , Baoxiang Pan , Linlu Mei
{"title":"Global reconstruction of three decades of fine-grained nighttime light data with analysis of large-scale infrastructure and landmarks","authors":"Jinyu Guo , Feng Zhang , Hang Zhao , Baoxiang Pan , Linlu Mei","doi":"10.1016/j.rse.2025.115036","DOIUrl":null,"url":null,"abstract":"<div><div>Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. However, global, long-term, and fine-grained nighttime light observations are lacking, hindering the analysis and application of continuous light changes in specific facilities over extended periods. To address this gap, we reformulated the super-resolution task – reconstructing low-resolution nighttime light data into high-resolution images – by explicitly incorporating temporal difference information. Rigorous validation across three evaluation years demonstrates that our dataset consistently outperforms existing products at global, national, and city scales. Case studies of high-value infrastructure sites, such as artificial islands and international airports, highlight our dataset’s unprecedented ability to capture both long-term gradual illumination changes associated with development and abrupt intensity oscillations caused by wars. The dataset is available at <span><span>https://doi.org/10.5281/zenodo.15845676</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115036"},"PeriodicalIF":11.4000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725004407","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. However, global, long-term, and fine-grained nighttime light observations are lacking, hindering the analysis and application of continuous light changes in specific facilities over extended periods. To address this gap, we reformulated the super-resolution task – reconstructing low-resolution nighttime light data into high-resolution images – by explicitly incorporating temporal difference information. Rigorous validation across three evaluation years demonstrates that our dataset consistently outperforms existing products at global, national, and city scales. Case studies of high-value infrastructure sites, such as artificial islands and international airports, highlight our dataset’s unprecedented ability to capture both long-term gradual illumination changes associated with development and abrupt intensity oscillations caused by wars. The dataset is available at https://doi.org/10.5281/zenodo.15845676.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.