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Remote Sensing Applications in Urban Environment and Climate

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

Deadline for manuscript submissions: 28 September 2025 | Viewed by 784

Special Issue Editors

Department of Statistics, Iowa State University, Ames, IA, USA
Interests: urban environment; urban climate; urban heat island; extreme heat; land use mapping

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Guest Editor
College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
Interests: thermal remote sensing; urban environment; urban microclimate; ecological environment

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Guest Editor
Center for Survey Statistics & Methodology, Department of Statistics, Lowa State University, Ames, IA, USA
Interests: environmental statistics; spatio-temporal models; machine learning; data integration

Special Issue Information

Dear Colleagues,

Urbanization has significantly altered the properties of urban surfaces, influencing local and regional climate and exacerbating environmental challenges such as the urban heat island effect, increasing air pollution, and the increased frequency and intensity of extreme weather events. These changes not only disrupt ecological balance but also pose risks to public health, infrastructure, and urban resilience. Remote sensing with its high-resolution, multi-temporal, and multi-spectral capabilities, has become a powerful tool for monitoring urban environmental and climate change, providing essential insights for urban sustainability planning and climate adaptation.

This Special Issue aims to highlight the applications of remote sensing technologies and present the latest research findings in understanding urban environmental and climatic challenges. We invite high-quality contributions which investigate multi-source datasets, innovative methodologies, and advanced geospatial techniques to assess urban climate dynamics and environment issues. Multi-city studies integrating remote sensing with GIS, numerical modeling and artificial intelligence are particularly encouraged.

Key themes of interest include, but are not limited to:

  • Urban expansion and land use/cover changes;
  • Thermal remote sensing and urban heat islands;
  • Air quality and pollution monitoring;
  • Urban green space and ecological environments;
  • Risk assessment of urban disasters and extreme situations;
  • Urban hydrological and water resource analysis;
  • Urban carbon emission and energy use;
  • Machine learning applications for urban environments.

Dr. Jia Hu
Prof. Dr. Yuyu Zhou
Dr. Ying-Bao Yang
Prof. Dr. Zhengyuan Zhu
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

  • land use/cover change
  • urban climate
  • urban sustainability
  • urban extreme events
  • environmental inequality
  • human–environmental interactions
  • urban ecology
  • building energy use

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Published Papers (1 paper)

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Research

20 pages, 5756 KiB  
Article
Stepwise Downscaling of ERA5-Land Reanalysis Air Temperature: A Case Study in Nanjing, China
by Xuelian Li, Guixin Zhang, Shanyou Zhu and Yongming Xu
Remote Sens. 2025, 17(12), 2063; https://doi.org/10.3390/rs17122063 - 15 Jun 2025
Viewed by 324
Abstract
Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of [...] Read more.
Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Urban Environment and Climate)
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