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Geodata Science and Spatial Analysis with Remote Sensing

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: 14 November 2025 | Viewed by 3343

Special Issue Editors


grade E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: image reconstruction; image denoising; image super-resolution; remote sensing image processing; data fusion and application
Special Issues, Collections and Topics in MDPI journals
College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: spatiotemporal data analysis and modeling; pollutant modeling and mapping; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Geospatial Research, Department of Geography, College of Arts and Sciences, The University of Georgia, Athens, GA, USA
Interests: remote sensing; unmanned aerial systems; development and applications of innovative methods for geospatial analysis; spectral bio-indicators; vegetation–climate interactions; time series analysis; geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global warming and climate change have become indisputable facts. The Earth has decisively entered the Anthropocene phase. A series of resource, environment, and ecology issues have arisen due to continuous anthropic activities, particularly industrialization and urbanization. For example, air pollutants and toxic metals have had significant negative effects on public health and the environment.  Sustainable development has been severely influenced by a variety of issues such as air contamination, water pollution, heavy metal pollution, greenhouse gas emissions, organic pollution, and microplastic pollution.

To address the above issues, it is necessary to consider the environmental parameters and resource conditions, analyze the dynamics of each parameter and resource, identify the main ecological and environmental issues on a regional and global scale, and predict the trends for these environmental elements. The outcomes can supply scientific support to achieving the Sustainable Development Goals (SDG) of the United Nations.

Remote sensing technology supplies a new perspective to monitoring environmental parameters and resources due to its capacity for fast detection, wide spatial coverage, and multiple spectral characteristics. Notably, the limited number of ground monitoring stations which can help to manage environmental issues can be effectively compensated for with the use of remote sensing technology. Remote sensing technology has been widely used to record and assess ecological parameters. Meanwhile, previous studies have also used nighttime light remote sensing images as a proxy to predict social–economic parameters such as population density, GDP, and electricity consumption. Furthermore, some previous studies took advantage of hyperspectral remote sensing to analyze the pollutant concentrations in soils, the air, vegetation, and water bodies. It is clear that remote sensing technology plays an important role in data collection and further spatial analysis. With an increasing number fine-resolution sensors being launched, it is clear that the role of remote sensing will be more significant in the future.

Hence, it is urgent that we conduct related research in the field of geodata science and spatial analysis with remote sensing. The objective of this Special Issue is to publish papers with new perspectives and viewpoints on the use of remote sensing techniques in the field of geodata science and spatial analysis. This Special Issue mainly seeks to utilize remote sensing techniques, spatial analysis methods, models, and numerical simulations to address complicated social, economic, resource, ecology, and environmental issues. Articles are not limited to the above-mentioned topics, and articles on other topics related to remote sensing are also encouraged.

Prof. Dr. Qiangqiang Yuan
Dr. Bin Guo
Dr. Sergio Bernardes
Guest Editors

Prof. Dr. Hailiang Jia
Guest Editor Assistant
Email:
College of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Homepage: https://scholar.google.com/citations?user=nxzyyWkAAAAJ&hl=zh-CN
Interests: rock mechanics; rock damage; hydraulic fracturing; heat and mass transfer in porous rock; rock weathering

Dr. Xiaowei Zhu
Guest Editor Assistant
Email:
Department of Mechanical and Materials Engineering, Portland State University, Portland, OR, USA
Homepage: https://www.pdx.edu/profile/xiaowei-luke-zhu
Interests: heat transfer; air pollutant dispersion; urban environments

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

  • air pollutants
  • heavy metals
  • nighttime light
  • data-driven models
  • hyperspectral remote sensing
  • coupled CFD-GIS

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Published Papers (3 papers)

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Research

19 pages, 7695 KiB  
Article
Multitemporal Analysis of Declassified Keyhole Imagery’ for Landuse Change Detection in China (1960~1984): A Python-Based Spatial Coverage and Automation Workflow
by Hao Li, Tao Wang, Weiqi Yao, Huanjun Liu, Chunyu Song and Jinyu Sun
Remote Sens. 2025, 17(5), 822; https://doi.org/10.3390/rs17050822 - 26 Feb 2025
Viewed by 374
Abstract
Keyhole imagery, acquired between the 1960s and 1980s, offers a unique opportunity to study land use changes prior to the era of modern remote sensing. This study evaluates the potential of free-download Keyhole imagery within China to detect land use changes over five [...] Read more.
Keyhole imagery, acquired between the 1960s and 1980s, offers a unique opportunity to study land use changes prior to the era of modern remote sensing. This study evaluates the potential of free-download Keyhole imagery within China to detect land use changes over five 5-year periods (1960–1984). Using metadata and spatial analysis tools in Python 3.12, we classified images into three resolution categories (meter-level, five-meter-level, and ten-meter-level) and analyzed their spatial distribution and repeated coverage. Results show that 26.5%, 58.9%, and 34.0% of areas were capable of detecting at least one land-use change event for the respective resolution categories. The T3 period (1970–1974) exhibited the greatest diversity of imagery combinations among the five periods. However, uneven spatial and temporal coverage, particularly in western and rural regions, limits the ability of free Keyhole imagery to conduct continuous multi-temporal analysis, and collaboration with paid Keyhole imagery could fill gaps in coverage and improve the accuracy of land use change detection. The study highlights the potential of Keyhole imagery for historical land use research while underscoring the need for methodological refinements to address data limitations. The shared Python scripts and metadata processing techniques could also support other land-use change research using Keyhole imagery globally. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis with Remote Sensing)
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19 pages, 9878 KiB  
Article
Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data
by Yachao Li, Tingting Liu, Zemin Wang, Mohammed Shokr, Menglin Yuan, Qiangqiang Yuan and Shiyu Wu
Remote Sens. 2024, 16(23), 4599; https://doi.org/10.3390/rs16234599 - 7 Dec 2024
Viewed by 876
Abstract
Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 [...] Read more.
Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 km and a high spatial resolution of 250 m. In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. We reduced the impact of using a constant emissivity on the IST retrieval accuracy by simulating the directional emissivity. Different ice surface types were used in the simulation, and we recommend the sun crust type as the most suitable for IST retrieval. We estimated the real-time water vapor content using a band ratio method from the MERSI-I near-infrared data. The results show that the retrieved IST was lower than the buoy measurements, with a mean bias and root-mean-square error (RMSE) of −1.928 K and 2.616 K. The retrieved IST is higher than the IceBridge measurements, with a mean bias and RMSE of 1.056 K and 1.760 K. Compared with the original algorithm, the developed algorithm has higher accuracy and reliability. The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis with Remote Sensing)
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15 pages, 7061 KiB  
Article
COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets
by Qiansi Tu, Frank Hase, Kai Qin, Carlos Alberti, Fan Lu, Ze Bian, Lixue Cao, Jiaxin Fang, Jiacheng Gu, Luoyao Guan, Yanwu Jiang, Hanshu Kang, Wang Liu, Yanqiu Liu, Lingxiao Lu, Yanan Shan, Yuze Si, Qing Xu and Chang Ye
Remote Sens. 2024, 16(21), 4022; https://doi.org/10.3390/rs16214022 - 29 Oct 2024
Viewed by 935
Abstract
This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework [...] Read more.
This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH4, which highlight the impact of coal mining emissions as a major CH4 source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO2 and XCH4 data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO2 and 15.40 ± 31.60 ppb for XCH4. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis with Remote Sensing)
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