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Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3127

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Special Issue Editors

Dr. Zihao Zheng

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Guest Editor

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: nighttime lighting remote sensing; regional habitats assessment; spatial mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Qiming Zheng

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Guest Editor

Department of Geography and Resource Management, The Chinese University of Hong Kong, New Territories, Hong Kong SAR, China
Interests: nighttime light imagery; urbanization; climate change mitigation; time series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The United Nations Sustainable Development Goals (SDGs) represent a global vision addressing the intertwined challenges of social equity, economic growth, and environmental sustainability. Achieving these goals requires innovative data sources and analytical approaches that can provide consistent, timely, and globally comparable insights into human activity and its environmental consequences.

Nighttime light (NTL) remote sensing has emerged as a uniquely powerful tool in this context. By capturing the rhythms of human presence after dark, NTL observations offer unprecedented opportunities to monitor urbanization processes, energy consumption, socioeconomic development, and ecological impacts. Their long-term continuity, global coverage, and integration potential make NTL products an essential component of research and decision-making frameworks for advancing the SDGs.

The aim of this Special Issue is to gather innovative studies that advance both the methodology and application of NTL data. Topics of interest include sensor calibration and validation, the development of new NTL products, integration with socioeconomic and geospatial datasets, and applied research on urban dynamics, poverty and inequality, energy use and carbon emissions, ecological monitoring, and other sustainability challenges. Through these contributions, the Special Issue seeks to highlight the versatility of NTL observations and their potential to support interdisciplinary research and evidence-based policy.

By fostering collaboration across remote sensing, geoinformatics, and sustainability science, this Special Issue aspires to serve as a platform for advancing the frontiers of NTL research and to inspire innovative pathways toward achieving the SDGs.

Dr. Zihao Zheng
Dr. Qiming Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

nighttime light remote sensing
Sustainable Development Goals (SDGs)
socioeconomic applications
urbanization
energy consumption and carbon emissions
poverty and inequality assessment
light pollution

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Published Papers (5 papers)

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Research

27 pages, 20862 KB

Open AccessArticle

Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data

by Nuo Xu, Xin Cao and Miaoying Chen

Remote Sens. 2026, 18(9), 1417; https://doi.org/10.3390/rs18091417 - 3 May 2026

Viewed by 169

Abstract

Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank’s System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs))

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19 pages, 7105 KB

Open AccessArticle

Monitoring Urban GDP Growth in China Using Time-Series Nightlight Satellite Data

by Yong Xu, Chunlan Guo, Yimeng Song, Zhenjie Yuan and Hong Zhu

Remote Sens. 2026, 18(8), 1247; https://doi.org/10.3390/rs18081247 - 20 Apr 2026

Viewed by 548

Abstract

Fast and accurate monitoring of economic dynamics is essential for evaluating economic policies and informing effective strategies, especially when timely official gross domestic product (GDP) statistics are unavailable. In this study, the capability of time-series nightlight satellite data to model urban economic activity was assessed by integrating it with observed GDP statistics. Experimental results showed that, when nightlight information was elaborately fused with core urban land-use data, urban GDP dynamics could be modeled with satisfactory accuracy. Model-based estimates indicated that China’s average GDP growth rate was approximately 4% across cities, with pronounced fluctuations during the pandemic, especially in 2020 and 2022. The proposed approach further quantified that the actual GDP losses in China due to the pandemic were about 1.5% of the total GDP, and the simulated results suggested that many coastal and industrial cities faced even more severe impacts than major provincial cities. Overall, the findings demonstrate the value of nighttime remote sensing for monitoring urban economic dynamics and support the transferability of the proposed approach to other countries with limited or delayed GDP statistics. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs))

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28 pages, 8904 KB

Open AccessArticle

Spatiotemporal Evolution and Driving Mechanisms of Nighttime Lights and Population Coupling Coordination in China

by Zibo Wang, Shengbo Chen and Yucheng Xu

Remote Sens. 2026, 18(5), 813; https://doi.org/10.3390/rs18050813 - 6 Mar 2026

Viewed by 598

Abstract

Accurately characterizing the relationship between nighttime human activity intensity and population distribution is essential for understanding urban development. This study proposes an integrated analytical framework that combines multilevel coupling quantification, regional trend detection, and interpretable machine learning to examine the Nighttime Lights and Population Coupling Coordination Degree (NPCCD) across China from 2012 to 2022. Based on this framework, NPCCD is evaluated from grid to regional level, and the characteristics of effective, persistent, and newly added coupled regions are identified. Twelve socioeconomic indicators are further constructed as explanatory variables to model NPCCD using machine learning algorithms, and Shapley Additive Explanations (SHAP) is applied to interpret the outputs. The results show that 49.07% of China’s overall NPCCD experienced steady improvement during the study period. Significant regional disparities were observed: in the eastern and central regions, more than 60% of grids fell into the improving category, whereas nearly half of the grids in the western and northeastern regions remained unchanged. Newly emerging coupling areas exhibited an average NPCCD of 0.03, markedly lower than the 0.07 observed in persistent effective areas, reflecting a mismatch between infrastructure development and population growth. Population density, human capital, industrial upgrading, and fiscal decentralization jointly explained 58.4% of the model’s variance and were identified as the major driving forces, each showing pronounced nonlinear and interaction effects. This study provides a quantitative framework for evaluating the coordination between nighttime lights and population distribution and offers insights for sustainable and balanced regional development. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs))

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22 pages, 7022 KB

Open AccessArticle

Mapping Spectral Composition of Nighttime Lighting in Urban Green Spaces Using SDGSAT-1 NTL Data and Google Earth Imagery

by Yuan Yuan, Zhiqiang Lu, Hongbo Liu, Boyang Wang, Yanni Xu, Zhirong Zhang, Jiahuan Li and Bin Wu

Remote Sens. 2026, 18(5), 732; https://doi.org/10.3390/rs18050732 - 28 Feb 2026

Viewed by 571

Abstract

Characterizing the spectral composition of artificial light at night (ALAN) within urban green spaces (UGS) is vital for ecological conservation, yet traditional sensors often lack the requisite spatial and spectral resolution for fine-scale analysis. To address this gap, this study leverages high-resolution multispectral nighttime light (NTL) data from the SDGSAT-1 to perform a fine-scale characterization of lighting across diverse UGS typologies. We developed UGS-STUNet, a semantic segmentation framework based on Swin Transformer architecture, to accurately extract five UGS categories from Google Earth imagery. Two specialized spectral indices, blue-to-green (B/G) and green-to-red (G/R) ratios, were derived from SDGSAT-1 NTL data to quantify the lighting’s spectral composition. Application in Shanghai demonstrated that UGS-STUNet achieved a precision of 85.72%, significantly outperforming existing methods. Our findings reveal that street trees are subjected to the highest red-light intensity and the lowest B/G and G/R ratios due to their proximity to roadway illumination. In contrast, forest patches and belts exhibit higher spectral ratios, indicating a relatively higher exposure to blue and green wavelengths. This study provides a robust and scalable method for monitoring the spectral quality of urban nightscapes, offering critical insights for sustainable urban planning and lighting mitigation strategies to safeguard global biodiversity and public health. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs))

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42 pages, 17863 KB

Open AccessArticle

Evolution of Urban Spatial Morphology and Its Driving Mechanisms in Fujian Province Based on Multi-Source Nighttime Light Remote Sensing

by Yuanmao Zheng, Kexin Yang, Hui Lin, Wei Zhao and Siyi Lv

Remote Sens. 2026, 18(2), 331; https://doi.org/10.3390/rs18020331 - 19 Jan 2026

Cited by 1 | Viewed by 669

Abstract

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Sustainable Development Goals (SDGs))

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