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Remote Sensing
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Monitoring Urban Environment and Temperature Change Using Remote Sensing
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Monitoring Urban Environment and Temperature Change Using Remote Sensing

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

Deadline for manuscript submissions: 28 April 2025 | Viewed by 620

Special Issue Editors

Dr. George Xian

E-Mail Website
Guest Editor
USGS Center for Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
Interests: remote sensing; land change; urban; urban heat island
Special Issues, Collections and Topics in MDPI journals
Dr. Peter Ibsen

E-Mail Website
Guest Editor
USGS Geosciences & Environmental Change Science Center, Denver, CO, USA
Interests: ecology; climate; remote sensing
Prof. Dr. Xiangming Xiao

E-Mail Website
Guest Editor
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
Interests: ecology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has been used in a wide range of Earth science research and applications. Satellite observations have routinely provide data that can be used to monitor and measure surface land cover and temperature changes. With recent advances in remote sensing technologies, multiple remote sensing sensors such as VIIRS, MODIS, Landsat, ECOSTRESS, and others on geostationary satellites continue to provide optical and TIR data to the science community. Moreover, advancement in machine learning and other novel modelling algorithms have allowed for greater ability to link satellite-derived data with airborne and terrestrial-based sensors to examine spatial and temporal patterns in urban heat exposure and land cover change. This Special Issue invites manuscripts that focus on using remote sensing data to monitor urban land cover, surface temperature, and their changes. Papers focusing on the utility of these data and other high-resolution images including Sentinel, WorldView, and PlanetScope in assessing the current and historical urban land cover, urban thermal condition, and surface urban heat island change will be most welcome. 

This special issue aligns with Remote Sensing’s scope to publish research papers, reviews, technical notes, and communications covering all aspects of remote sensing science, from sensor design and validation/calibration to its application in geosciences, environmental sciences, ecology, and civil engineering. Our aim is to publish novel/improved methods/approaches and/or algorithms of remote sensing to benefit the community.

This Special Issue focuses on themes including urban environment, urban land cover and change, urban heat island, and climate.

Dr. George Xian
Dr. Peter Ibsen
Prof. Dr. Xiangming Xiao
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

  • urban
  • land cover
  • surface temperature
  • urban thermal environment
  • urban heat island
  • urban climate

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

27 pages, 14009 KiB  
Article
Model Development for Estimating Sub-Daily Urban Air Temperature Patterns in China Using Land Surface Temperature and Auxiliary Data from 2013 to 2023
by Yuchen Guo, János Unger and Tamás Gál
Remote Sens. 2024, 16(24), 4675; https://doi.org/10.3390/rs16244675 - 14 Dec 2024
Viewed by 424
Abstract
Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with [...] Read more.
Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R2 = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R2 = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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