Special Issue "Urban Heat Island Remote Sensing"

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

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editors

Prof. Dr. José A. Sobrino
Website SciProfiles
Guest Editor
Imaging Processing Laboratory, Universidad de Valencia, E-46071 Valencia, Spain
Interests: atmospheric correction in visible and infrared domains; retrieval of emissivity; surface temperature; evapotranspiration; thermal inertia; atmospheric water vapor from satellite image; the development of remote sensing methods for land cover dynamic monitoring
Special Issues and Collections in MDPI journals
Dr. Guillem Sòria
Website
Guest Editor
Global Change Unit, Image Processing Laboratory, Parque Científico, University of Valencia, C/ Catedrático José Beltran nº 2, 46980 Paterna, Spain
Interests: thermal remote sensing; retrieval of emissivity; surface temperature; calibration; validation with in situ data; surface urban heat island

Special Issue Information

Dear Colleagues,

In 2017, more than 50% of the world population lived in cities (Population Reference Bureau, 2017). This percentage is increasing every year as the world is being urbanized and, therefore, natural surfaces are being replaced by artificial ones with different thermal properties. As the urbanized surface grows, changes in the local climate occur. The Urban Heat Island (UHI) phenomenon is one example of this local climate change. This effect is characterized by the heating of urban zones in comparison to its non-urbanized surroundings. The effect is most relevant at night when urban surfaces, with higher heat capacities than rural surfaces, release energy that has been stored during the daytime with less efficiency than do the rural areas. Among the local impacts of the UHI phenomenon, those who stand out are the influence on the energy consumption, mainly in hot climate regions where the use of air-conditioning is increasing. Moreover, higher urban temperatures can increase the amount of urban smog that is formed, raising the level of air pollution. Finally, one of the most important impacts is the influence on human health.

Remote sensing data are a powerful tool to study the urban environment. The data provide the information for a city, covering the entire urban area at the same time. This is one of the advantages of using remote sensing data over the use of conventional data that are registered at urban meteorological stations. In addition, the meteorological network inside a city is not always as complete as desirable, and stations are not always evenly distributed spatially within the city. Consequently, some large areas may remain without coverage.

The main focus of this Special Issue is to compile the state-of-the-art in Urban Heat Island applications using remote sensing data. The Special Issue should serve as a medium to present and discuss both potential and challenges for future research in this area.

Related References

  1. Oke, T.R. Canyon geometry and the nocturnal urban heat island: Comparison of scale model and field observations. Clim. 1981, 1, 237–254.
  2. Voogt, J.A. Urban heat island. In Munn (Ed.), Encyclopedia of Global Environmental Change,.2002, Wiley: Chichester, UK, pp. 660–666.
  3. Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ. 2003, 86, 370–384.
  4. Weng, Q. H.; Lu, D.S.; Schubring, J. Estimation of land surface temperature–Vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483.
  5. Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 2007, 106, 375–386.
  6. Rajasekar, U.; Weng, Q.H. Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM plus imagery. J. Remote Sens. 2009, 30, 3531–3548.
  7. Sobrino, J.A.; Bianchi, R.; Paganini, M.; Sòria, G.; Jimenez-Munoz, J.C.; Oltra-Carrió, R.; et al. Dual-use European Security IR Experiment 2008 (DESIREX 2008) Final Report. European Space Agency: Frascati, Italy, 2009.
  8. Sobrino, J.A.; Oltra-Carrió, R.; Sòria, G.; Bianchi, R.; Paganini, M. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens. Environ. 2012, 117, 50–56.
  9. Oltra-Carrió, R.; Sobrino, J.A.; Franch, B.; Nerry, F. Land surface emissivity retrieval from airborne sensor over urban areas, Remote Sens. Environ. 2012, 123, 298–305.
  10. Sobrino, J.A.R.; Oltra-Carrió, G.; Sòria, J.C.; Jiménez-Muñoz, B.; Franch, V.; Hidalgo, C.; Mattar, Y.; Julien, J.; Cuenca, M.; Romaguera, J.A.; et al. Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. J. Remote Sens. 2013, 34, 3177–3192.
  11. Oltra-Carrio, R.; Cuberó-Castan, M.; Briottet, X.; Sobrino, J.A. Analysis of the performance of the TES algorithm over urban areas. IEEE Trans. Geos. Remote Sens. 2014, 52,6989–6998.

Dr. Jose A. Sobrino
Dr. Guillem Sòria
Guest Editors

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Keywords

  • Urban Heat Island
  • Land Surface Temperature
  • Remote Sensing
  • Environment

Published Papers (9 papers)

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Research

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Open AccessArticle
Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China
Remote Sens. 2019, 11(15), 1802; https://doi.org/10.3390/rs11151802 - 01 Aug 2019
Cited by 5
Abstract
The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, [...] Read more.
The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data
Remote Sens. 2019, 11(7), 767; https://doi.org/10.3390/rs11070767 - 29 Mar 2019
Cited by 3
Abstract
Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide [...] Read more.
Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1.23 K for Tmax and 1.20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1.21 K for Tmax and 1.27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands
Remote Sens. 2019, 11(3), 299; https://doi.org/10.3390/rs11030299 - 01 Feb 2019
Cited by 17
Abstract
Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat [...] Read more.
Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat island effect. This phenomenon has recently been taken into account by local elected officials. As a result, developers have decided to use solutions in redevelopment projects to combat high temperatures in urban areas. Consequently, the objective is to study the land-surface temperature evolution of six main urban redevelopments in Lyon, France, from 2000 to 2017. Three of them (the Confluence, Kaplan, and Museum sites) were composed of industrial areas that have undergone major transformations and are now tertiary or residential areas. Two sites have been more lightly transformed, particularly by increasing vegetation to reduce heat stress and urban flooding (Dock and Garibaldi Street). Finally, the Groupama Stadium has been built into agricultural and wooded areas. Changes in vegetation cover (NDVI), water (MNDWI), and moisture (NDMI) content, built areas (NDBI) and bare soil (NDBaI) are also monitored. The results show that the Confluence and Kaplan sites were accompanied by a decrease in surface temperature and an increase in vegetation and moisture, whereas the Groupama Stadium displayed a rise in surface temperature and a decrease in vegetation. On the other hand, the Museum, Dock, and Garibaldi sites did not exhibit clear and uniform trends, although an increase in surface temperature was shown in some statistical tests. The disparity of the results shows the necessity to include a significant amount of vegetation during redevelopment operations in order to reduce heat stress. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
Satellite-Based Spatiotemporal Trends of Canopy Urban Heat Islands and Associated Drivers in China’s 32 Major Cities
by Long Li and Yong Zha
Remote Sens. 2019, 11(1), 102; https://doi.org/10.3390/rs11010102 - 08 Jan 2019
Cited by 8
Abstract
The urban heat island (UHI) effect, in which urbanized areas tend to have warmer conditions compared to their rural surroundings, has drawn increasing attention in recent years. Using ground-based and satellite remote sensing data, we present a method to quantify the spatial pattern [...] Read more.
The urban heat island (UHI) effect, in which urbanized areas tend to have warmer conditions compared to their rural surroundings, has drawn increasing attention in recent years. Using ground-based and satellite remote sensing data, we present a method to quantify the spatial pattern and diurnal and seasonal variations in canopy layer heat islands (CLHIs) in China’s 32 major cities during 2009 and investigate their relationships with built-up intensity (BI), nighttime lights, vegetation activity, surface albedo, and surface urban heat island intensity (SUHII). The results show that both the annual daytime and nighttime CLHI intensities (CLHIIs) were positive ranging from 0.2 °C to 2.2 °C and from 0.3 °C to 2.4 °C for these major cities, respectively. Higher CLHIIs were observed in the night, especially for northern parts of China. Along urban–rural gradients, the CLHI effect had an exponential decay shape and differed greatly by season. The CLHII distribution correlated positively and significantly to BI and nighttime lights. Vegetation activity was negatively correlated with the CLHII and more strongly in summer. Surface albedo showed an extremely weak correlation with the CLHII. In addition, CLHII had a strong correlation with SUHII. The annual daytime SUHII was 1.2 ± 1.1 °C (mean ± standard deviation) with 0.40 °C (95% confidence interval 0.36 to 0.44 °C) of annual daytime CLHII. The annual nighttime SUHII was 2.0 ± 0.8 °C with 1.04 °C (0.99 to 1.09 °C) of annual nighttime CLHII. Our results suggest that, reducing built-up intensity and anthropogenic heat emissions and increasing urban vegetation provide a co-benefit of mitigating SUHI and CLHI effects. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon
Remote Sens. 2018, 10(9), 1428; https://doi.org/10.3390/rs10091428 - 07 Sep 2018
Cited by 23
Abstract
This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 [...] Read more.
This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
Correction for the Impact of the Surface Characteristics on the Estimation of the Effective Emissivity at Fine Resolution in Urban Areas
Remote Sens. 2018, 10(5), 746; https://doi.org/10.3390/rs10050746 - 11 May 2018
Cited by 2
Abstract
Most of the methods used to retrieve land surface temperature (LST) from thermal infrared (TIR) satellite data in urban areas do not take into account the complexity of the surface. Cities are characterized by high surface roughness and one of the main constraints [...] Read more.
Most of the methods used to retrieve land surface temperature (LST) from thermal infrared (TIR) satellite data in urban areas do not take into account the complexity of the surface. Cities are characterized by high surface roughness and one of the main constraints to estimate LST over those areas is the difficulty to define an effective emissivity for a given pixel at a given scale. When working with mixed pixels, the emissivity used to estimate the LST is an effective emissivity composed of the emissivities of each basic element constituting the pixel. In urban areas, the surface geometry has a strong impact on this effective emissivity. Its estimation from TIR satellite data must be carried out considering multiple surface reflections and diffusions within the urban canopy in order to retrieve accurate LST values. The objective of this study is then to evaluate the impact of the surface geometry within the pixel on effective emissivity estimation and to propose a method to derive an effective emissivity corrected for those effects. Emissivity can be derived at 90 m of spatial resolution from the TIR data acquired by ASTER. To evaluate the impact of the geometry at the scale of an ASTER pixel, several urban canyon configurations are designed to develop and test the correction method. The basic principle behind the method is to accurately estimate the downwelling TIR radiation received by a pixel integrating contributions from both the atmosphere and the scene inside this pixel and then derive the corrected effective emissivity from ASTER data using the TES (temperature emissivity separation) algorithm. First, the total downwelling TIR radiation is estimated from the geometric characteristics of the scene, using morphological indicators and integrating the non-isothermal behavior of the pixel thanks to 3D thermo-radiative model simulations. The validation of those estimations for each canyon configuration provides a maximum RMSE (Root Mean Square Error) value of 2.2 W·m−2. The validation performed over a district extracted from the 3D numerical model of Strasbourg (France) shows a RMSE of 2.5 W·m−2. Once the method to estimate the total downwelling TIR radiation is validated, LSE and LST maps are retrieved from an ASTER image over three districts of Strasbourg, showing that accounting for the surface geometry highlights thermal behavior differences inside districts, and that the impact of the geometry seems more influenced by building height than street width or building density. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand
Remote Sens. 2018, 10(5), 665; https://doi.org/10.3390/rs10050665 - 24 Apr 2018
Cited by 13
Abstract
For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand’s six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales—diurnal, seasonal, and multiyear. [...] Read more.
For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand’s six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales—diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003–2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 °C and 6 °C; during nighttime, the intensities are rather similar for all the six cities, between 1 °C and 2 °C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Open AccessArticle
Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery
Remote Sens. 2018, 10(3), 440; https://doi.org/10.3390/rs10030440 - 11 Mar 2018
Cited by 16
Abstract
Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this [...] Read more.
Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this work deals with the SUHI pattern identification within the different land use categories of Bangkok city plan. This study first provides an overview of the SUHI phenomenon in Bangkok, then singles out the surface heating behavior in each land use category. To describe the SUHI dynamics within the different classes, the main statistics of the SUHI intensity (mean, standard deviation, maximum and minimum) are computed. Overall, the analysis points out that the categories placed in the city core (high-density residential; commercial; historical and military classes) exhibit the highest mean SUHI intensities (around 4 °C); whilst the vegetated pixels exert a less cool effect with respect to the greenery of categories mainly placed farther from the city center. The proposed analysis can help to identify if the land use plan requires targeted future actions for the SUHI mitigation; or if the maintenance of the current urban development model is in line with the environmental sustainability. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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Review

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Open AccessReview
Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
Remote Sens. 2019, 11(1), 48; https://doi.org/10.3390/rs11010048 - 29 Dec 2018
Cited by 92
Abstract
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have [...] Read more.
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling. Full article
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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