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Remote Sensing
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Big Earth Data in Support of the Sustainable Development Goals
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Big Earth Data in Support of the Sustainable Development Goals

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2083

Special Issue Editors

Dr. Minquan Wu

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing of engineering projects; remote sensing for sustainable development goals; energy; infrastructure remote sensing; belt and road initiative; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xi Li

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: remote sensing image processing; night-time light remote sensing; remote sensing applications in social sciences; national geographical conditions monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Chen

E-Mail Website
Guest Editor
1. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: big data

Special Issue Information

Dear Colleagues,

A lack of critical data constrains the effective implementation of the 2030 Agenda. As of 2023, internationally comparable data for the 17 SDGs have not achieved global coverage, and only 9 targets have internationally comparable data for half of the world's countries or regions. There are also significant gaps in data timeliness. In this context, about half of the countries are willing to use Earth observation satellite imagery and internet-based data collection methods to address data gaps. Expanding the range of data sources and innovating approaches to data sources through using rapidly developing Big Data and Artificial Intelligence (AI) technologies—thereby monitoring global SDG progress in a timely and accurate manner and allowing us to formulate more effective action plans—will be one of the key priorities for bridging data gaps and accelerating the process of realizing the SDGs.

This Special Issue aims to investigate the monitoring and evaluation methods of BigEarth data for different SDG goals—particularly in SDG 7 (Affordable and Clean Energy)—Integrated Evaluations, and Interactions among the SDGs. Articles may cover any content related to BigEarth data monitoring and analyses of SDG indicators, including new monitoring methods for various SDG indicators, implementation paths for indicators, cross-cutting and comprehensive analyses of indicators, etc. In addition to BigEarth data methods and tools such as remote sensing and GIS, we also welcome the comprehensive integration of remote sensing research with geographic information, statistical data, and other data.

Articles may cover themes including, but not limited to, the following topics:

  • Access to electricity;
  • Clean cooking;
  • Renewable energy;
  • Energy efficiency;
  • International cooperation on energy;
  • Comprehensive indicators of SDGs;
  • Cross-cutting indicators of SDGs.

Dr. Minquan Wu
Prof. Dr. Xi Li
Dr. Yu Chen
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • affordable and clean energy
  • integrated evaluations and interactions among SDGs
  • access to electricity
  • clean cooking
  • renewable energy
  • energy efficiency

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Published Papers (3 papers)

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Research

30 pages, 5923 KiB  
Article
Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Social Factors
by Cuiting Li, Dongmei Yan, Shuo Chen, Jun Yan, Wanrong Wu and Xiaowei Wang
Remote Sens. 2025, 17(5), 865; https://doi.org/10.3390/rs17050865 - 28 Feb 2025
Viewed by 471
Abstract
Analyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based [...] Read more.
Analyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based on the K-Means clustering algorithm combined with multiple indicators integrated with socio-economic factors. Combining IPAT theory, regional GDP and population density, the final EPC prediction models were developed. Using these models, the EPC distributions for Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations in 2017–2021 were generated at both the administrative district level and the 1 km × 1 km grid scale. The spatio-temporal dynamics of the EPC distribution in these urban agglomerations during this period were then analyzed, followed by EPC predictions for 2022. The models showed a significant improvement in prediction accuracy, with the average MARE decreasing from 30.52% to 7.60%, 25.61% to 11.08% and 18.24% to 12.85% for the three urban agglomerations, respectively; EPC clusters were identified in these areas, mainly concentrated in Langfang and Chengde, Shanghai and Suzhou, and Dongguan; from 2017 to 2021, the EPC values of the three urban agglomerations show a growth trend and the distribution patterns were consistent with their economic development and population density; the R2 values and the statistical values for the 2022 EPC predictions using the improved classification EPC models reached 0.9692, 0.9903 and 0.9677, respectively, confirming that the proposed method can effectively predict the EPC of urban agglomerations and is applicable in various scenarios. This method provides a timely and accurate spatial update of EPC dynamics, offering fine-scale characterization of urban EPC patterns using night light images. Full article
(This article belongs to the Special Issue Big Earth Data in Support of the Sustainable Development Goals)
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26 pages, 19741 KiB  
Article
Remote Sensing Identification and Analysis of Global Building Electrification (2012–2023)
by Shengya Ou, Mingquan Wu, Zheng Niu, Fang Chen, Jie Liu, Meng Wang and Dinghui Tian
Remote Sens. 2025, 17(5), 777; https://doi.org/10.3390/rs17050777 - 23 Feb 2025
Viewed by 605
Abstract
The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in [...] Read more.
The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in terms of temporal discontinuity and restricted threshold selection. To effectively address these issues, in this work, an improved remote sensing method was proposed to monitor global building electrification. By integrating global land cover data, built-up area data, and annual NPP/VIIRS nighttime light images, a regional threshold method was used to identify electrified and unelectrified areas yearly, generating a global building electrification dataset for 2012–2023. Based on our analysis, we found the following: (1) The five assessment metrics of the product—Accuracy (0.9856), Precision (0.9734), Recall (0.9984), F1-score (0.9858), and Matthews Correlation Coefficient (0.9715)—all exceed 0.9, demonstrating that our method achieves high reliability in identifying electrified buildings. (2) In 2023, 91.88% of global building areas were electrified, with the unelectrified buildings being predominantly located in rural regions of developing countries. (3) Between 2012 and 2023, the global electrified building area increased by 2.4199 million km2, with rural areas experiencing a faster growth rate than town areas. The annual reduction rate of unelectrified building area was 0.62%. However, to achieve universal electricity access by 2030, this rate must nearly double. (4) External factors such as the COVID-19 pandemic, extreme weather events, and armed conflicts significantly affect global electrification progress, with developing countries being particularly vulnerable. In our work, remote sensing methodologies and datasets for monitoring electrification trends were refined, and a detailed spatial representation of unelectrified areas worldwide was provided. Full article
(This article belongs to the Special Issue Big Earth Data in Support of the Sustainable Development Goals)
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21 pages, 20266 KiB  
Article
Spatiotemporal Variation in Carbon and Water Use Efficiency and Their Influencing Variables Based on Remote Sensing Data in the Nanling Mountains Region
by Sha Lei, Ping Zhou, Jiaying Lin, Zhaowei Tan, Junxiang Huang, Ping Yan and Hui Chen
Remote Sens. 2025, 17(4), 648; https://doi.org/10.3390/rs17040648 - 14 Feb 2025
Viewed by 560
Abstract
A comprehensive evaluation of the variations in carbon use efficiency (CUE) and water use efficiency (WUE) in the Nanling Mountains Region (NMR) is crucial for gaining insights into the intricate relationships between climate change and ecosystem processes. This study evaluates the spatiotemporal rates [...] Read more.
A comprehensive evaluation of the variations in carbon use efficiency (CUE) and water use efficiency (WUE) in the Nanling Mountains Region (NMR) is crucial for gaining insights into the intricate relationships between climate change and ecosystem processes. This study evaluates the spatiotemporal rates of dynamics in CUE, WUE, gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) over the period from 2001 to 2023, using remote sensing data and linear regression analysis. Trend analysis, Hurst exponent analysis, and stability analysis were applied to examine the long-term patterns of CUE and WUE, while partial correlation analysis was employed to explore the spatial relationships between these efficiencies and climatic factors. The main findings of the study are as follows: (1) The CUE and WUE of the NMR decreased geographically from 2001 to 2023, and both the CUE and WUE of NMR showed a significant declining trend (p < 0.05) with the CUE decreasing at a rate of 0.0014/a (a: year) and the WUE falling at a rate of 0.0022/a. (2) The average values of the CUE and WUE of the NMR from 2001 to 2023 were 0.47 and 0.82 g C·m−2·mm−1, respectively, with a clear geographical difference. (3) The CUE and WUE in the NMR showed widespread degradation trends with some localized improvements, yet sustainability analysis indicates a likely continued decline across most areas, particularly for forests, while grasslands exhibit the greatest resilience. (4) Precipitation had a significantly stronger impact on WUE, while temperature appeared to exert a more substantial effect on CUE, with vegetation types responding differently; notably, shrubland displayed a direct association between CUE and temperature. In summary, multi-source data were employed to comprehensively analyze the spatiotemporal dynamics of CUE and WUE in the NMR over the past 23 years. We also examined the features of their responses to global warming, offering valuable theoretical insights into the carbon and water dynamics within the terrestrial ecosystems of the NMR. Full article
(This article belongs to the Special Issue Big Earth Data in Support of the Sustainable Development Goals)
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