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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: closed (28 April 2026) | Viewed by 12108

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 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-anonymized 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 policies can be found here.

Published Papers (5 papers)

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Research

32 pages, 4528 KB  
Article
Diurnal Asymmetry and Risk Amplification of Surface Urban Heat Island and Extreme Heat in the Yangtze River Basin (2001–2020)
by Hongji Zhu, Haokai Wang and Rui Yao
Remote Sens. 2026, 18(8), 1236; https://doi.org/10.3390/rs18081236 - 19 Apr 2026
Viewed by 862
Abstract
Against the backdrop of global climate warming and rapid urbanization, urban thermal environments exhibit strong spatiotemporal heterogeneity and diurnal contrasts. Based on the high-resolution, seamless land surface temperature dataset (GSHTD), this study systematically evaluates the evolution of extreme urban thermal environments across 107 [...] Read more.
Against the backdrop of global climate warming and rapid urbanization, urban thermal environments exhibit strong spatiotemporal heterogeneity and diurnal contrasts. Based on the high-resolution, seamless land surface temperature dataset (GSHTD), this study systematically evaluates the evolution of extreme urban thermal environments across 107 cities in the Yangtze River Basin (YRB) from 2001 to 2020. Urban cores were delineated using high-density impervious surface area (ISA ≥ 50%), and rural background temperatures were elevation-corrected. To quantify the asynchrony between extreme heat intensification and seasonal background warming, we propose “Risk Amplification Index (Ri)”. The results reveal that: (1) The surface urban heat island intensity (SUHII) intensified across the entire basin, with daytime increases being significantly stronger and more spatially consistent than nighttime ones. (2) The intra-annual SUHII cycle exhibits a unimodal pattern peaking in August, with widening inter-city disparities during the warm season. (3) The intensification of extreme heat is often asynchronous with background warming. Combined with land-use change intensity (ΔISA), our analysis indicates that small and medium-sized cities undergoing rapid expansion (high ΔISA) exhibit a stronger heat-risk amplification effect (higher Ri), whereas mature megacities (high total ISA but low ΔISA) show relatively synchronous thermal evolution. The results suggest that an ISA density of around 70% may act as a threshold beyond which extreme-heat amplification is more likely to intensify. These findings suggest that future heat-risk governance should be time- and region-specific, shifting the focus of climate-adaptive planning from solely megacities to mitigating extreme-heat risk amplification during the rapid urbanization of small and medium-sized cities. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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25 pages, 3441 KB  
Article
The Surface Is Not Superficial: Utilizing Hyper-Local Thermal Photogrammetry for Pedestrian Thermal Comfort Inquiry
by Logan Steinharter, Peter C. Ibsen, Priyanka deSouza and Melissa R. McHale
Remote Sens. 2026, 18(2), 348; https://doi.org/10.3390/rs18020348 - 20 Jan 2026
Cited by 3 | Viewed by 872
Abstract
The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface [...] Read more.
The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (TS) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution TS collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR TS averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR TS strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR TS explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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19 pages, 3620 KB  
Article
Surface Urban Heat Island Risk Index Computation Using Remote-Sensed Data and Meta Population Dataset on Naples Urban Area (Italy)
by Massimo Musacchio, Alessia Scalabrini, Malvina Silvestri, Federico Rabuffi and Antonio Costanzo
Remote Sens. 2025, 17(19), 3306; https://doi.org/10.3390/rs17193306 - 26 Sep 2025
Cited by 1 | Viewed by 2196
Abstract
Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to [...] Read more.
Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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26 pages, 2906 KB  
Article
Street-Scale Urban Air Temperatures Predicted by Simple High-Resolution Cover- and Shade-Weighted Surface Temperature Mosaics in a Variety of Residential Neighborhoods
by Katarina Kubiniec, Kevan B. Moffett and Kyle Blount
Remote Sens. 2025, 17(11), 1932; https://doi.org/10.3390/rs17111932 - 3 Jun 2025
Cited by 4 | Viewed by 3741
Abstract
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is [...] Read more.
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LSTg) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (Ts), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated Ts values and sun-shielded air temperatures (Ta). In contrast, relationships of Ta to tree coverage, impervious area, or the LSTg of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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27 pages, 14009 KB  
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
Cited by 2 | Viewed by 3063
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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