*2.1. Urban Heat Island*

UHI studies have been conducted for over 200 years, since the first conceptualization by Luke Howard in 1818 [98]. Generally, an urban heat island (UHI) is an urban area or metropolitan area that is significantly warmer than its surrounding rural areas because of human activities. The temperature difference is usually greater at night than during the day and is most apparent when winds are weak. Some research [99,100] shows that the annual mean air temperature of a city with 1 million people or more can be 1–3 ◦C warmer than its surroundings. In the evening, the difference can be as high as 12 ◦C. Heat islands can affect

communities by increasing the summertime peak energy demand (such as air conditioning costs), air pollution and greenhouse gas emissions, heat-related illness, and mortality, and decreasing water quality and ecosystem security. Higher temperature "domes" are created over an urban or industrial areas by hot air layers forming at building-top or chimney-top level. This dome is about 5 ◦C to 7 ◦C warmer than the air above it and the ground level temperature, and can trap all polluting emissions within its confines (see also temperature inversion [53,57]).

The large amount of heat generated from urban structures and pavements, as they absorb and re-radiate solar radiation, as well as the heat from other anthropogenic sources, are the main causes of UHI. These heat sources increase the temperatures of an urban area compared with its surroundings, which is known as UHI intensity (UHII). Traditionally, regardless of the methodology employed, whether it refers to (1) differences between two fixed observatories, one urban and another peripheral or non-urban; (2) mobile urban transects; or (3) remote sensing analysis, UHII provides a value of thermal differences between contrasted points, sectors, or areas, one urban and another that could be termed non-urban. Thus, the intensity of the UHI is seen in the temperature difference expressed at a given time between the hottest sector (areas) of the city and the surrounding non-urban space. The intensity of the heat island is the simplest and most quantitative indicator of the thermal modification imposed by the city upon the territory in which it is situated and of its relative warming in relation to the surrounding rural environment. The intensity could be defined for various time scales and geographical locations [101,102].

### *2.2. The Study of the Spatial Structure of Urban Thermal Patterns, Change Dynamics, and Their Relation to Urban Surface Characteristics*

Based on the fractional theory of ecology [103,104], the spatial structure of urban thermal patterns and temporal change dynamics can be studied in two and three dimensions. Figure 1 shows an example of the UHI and UHIRIP profile in Sioux Falls, South Dakota, USA, and the surrounding area, derived from Landsat ARD LST over different land cover classes [97]. The study of temporal change in UHI can include multiple scales of change, including daily, day and night, monthly, seasonal, yearly, and long-term time series. The physical mechanisms driving UHI are well documented [28]. UHIRIP may be described in multiple ways with various methodological approaches to investigate each type; specifically, it can impact the ground, the surface, and various heights in the air [105,106] at a regional scale. Different pictures arise for each type of UHI when measured by different methods. Tam et al. [107] suggested that the magnitude of total change in day to day temperature variability can be used to decide a suitable urban/rural pair for any urbanization impact study. Generally, the UHI at a regional scale is best measured using remotely sensed data with one or multiple thermal bands. When explaining the character of remotely sensed UHI, Roth et al. [108] assert, "satellite-derived surface heat islands are in a separate class and it is not clear that they will match others measured by more conventional means in the urban canopy layer or the urban boundary layer". Their precautionary statement relates in part to the surface "seen" by remote sensing platforms that depend on altitude and the camera or sensor angle. Imagery collected at nadir and/or high altitude primarily consists of rooftops, streets, crop fields, and vegetation canopies. Observations from lower heights at oblique angles consist of items seen from a bird's-eye perspective plus varying degrees of vertical features in the landscape, such as the walls of buildings. As a result, angle can have a large influence on the urban surface temperatures recorded by airborne and spaceborne thermal infrared sensors [109]. Another concern regards mixed pixels (i.e., individual pixels containing surfaces having different physical properties, depending on the spatial resolution of the data), which can complicate image analysis. This is especially true for thermal sensors aboard satellites, because most have a spatial resolution that is coarser than the other spectral bands on the satellite. The typical variation of urban surface properties also complicates thermal sensors. A final consideration when using remotely sensed imagery involves correcting for atmospheric attenuation. For many applications, these issues are far outweighed by remote sensing's

benefits. With high spatial resolution thermal data, these issues can typically be resolved. Additionally, from a macro research perspective, remotely sensed thermal data have the major advantage of investigating UHI and UHIRIP at a broad scale, permitting focus on environmental issues in urban agglomerations and surrounding areas, and at urban and non-urban interfaces.

**Figure 1.** An example of UHI and UHIRIP in the urban and the urban and non-urban interface for part of the Sioux Falls, SD area.

### *2.3. Simulation and Projection of UHI and UHIRIP*

Applying theories of landscape ecology [104], UHI studies focus on moving from static spatial structures of urban thermal patterns to the change dynamic of spatial patterns and processes of urban thermal characteristics. The spatial structure of UHI patterns determines the processes of UHI impacts. Li et al. [110] simulated the urban climate of various generated cities under the same weather conditions. By studying various city shapes, they generalized and proposed a reduced form to estimate UHI intensities based only on the structure of urban sites, as well as their relative distances. They concluded that in addition to the size, the UHI intensity of a city is directly related to the density and the amplifying effect that urban sites have on each other. Their approach can serve as a UHI rule of thumb for the comparison of urban development scenarios. Ramírez-Aguilar and Lucas Souza [111] present a study based on the relationship between UHI and population size (p) by considering the population density (PD) and the urban form parameters of different neighborhoods in the city of Bogotá, Colombia. They concluded that urban form, expressed by land cover and urban morphology changes caused by population density, has a grea<sup>t</sup> effect on temperature differences within a city. Advances in computing technology have fostered the development of new and powerful deep learning techniques that have demonstrated promising results in a wide range of applications. In particular, deep learning methods have been successfully used to classify remotely sensed data collected by Earth observation instruments [112]. Deep learning algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area, and have been introduced into the geoscience and remote sensing community for remotely sensed big data analysis [113]. With climate change, the simulation and projection of UHI and its regional impact by using computer

technology (deep learning) and remotely sensed data are becoming more important for urban planning and policy makers.

### *2.4. Challenges for Land Surface Temperature and Emissivity Retrieval (Separation)*

Land surface temperature and emissivity are two important surface parameters that can be derived from remotely sensed data after atmospheric correction [114–116]. Besides radiometric calibration and cloud detection, two main problems need to be resolved in order to obtain land surface temperature and emissivity values from various satellite sensors. These problems are often referred to as land surface temperature and emissivity separation (TES) from radiance at ground level, and as atmospheric corrections in the literature [117,118]. Reliable retrieval of urban and intra-urban thermal characteristics using satellite thermal data depends on accurate removal of the effects of atmospheric attenuations, as well as angular and land surface emissivity. In the thermal infrared of remotely sensed data, the emission of the targets is dominant when compared with the reflection, and this radiation is a function of two unknowns—the emissivity and the temperature of the target [119]. The temperature and emissivity separation is complex because of the existing non-linear relationship between temperature and radiance. The complex dynamics of these relationships within the target (atomic level) propagates in a cascade effect, reflecting variations in determining emissivity. Mohamed et al. [117] reviewed details of LST and land surface emissivity (LSE) retrieval methods and their potential for adoption in medium spatial resolution, including ASTER and Landsat. The review further comments on spatial and temporal prospects of effective intra-urban surface thermal mapping. They also suggested future development of land surface temperature and emissivity estimation for UHI assessment. Li et al. [120] described the theoretical basis of LSE measurements and reviewed the published methods. They also categorized these methods into (1) (semi-) empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). Then, they discussed the validation methods that are important for verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given. These studies provided a forum for assessing what had been achieved by the UHI community over four decades, and what needs to be done in the near future. It is clear that the observation, experiments, and algorithm development efforts, although completely worthwhile for scientists, need to deliver various datasets, especially from remotely sensed data to modelers working in the areas of UHI and UHIRIP at local, regional, and global levels. A lot of basic theoretical research and scientific verification work has been done on scale issues, as well as scaling issues including emissivity and temperature measurements related to remote sensing standards [121]. All of the methods described in Rolim et al. [119] represent the largest and main part of the existing methods of temperature and emissivity separation developed in the last four decades, but further research is necessary for more precise methods that are less susceptible to errors during the separation of these variables.

### *2.5. The Relationship between Atmospheric Heat Islands and Surface UHI through Combining Coincident Remote Sensing and Ground-Based Observations*

Generally, UHI data are obtained from one of two sources—weather stations and remote sensing. Remotely sensed data have been used to observe how UHI impacts climate change in urban and non-urban areas for decades because of the multiple temporal and spatial resolutions of remotely sensed datasets. Hundreds of published studies explore UHI and its impacts by using these two data sources, but the relationship between air temperature obtained from field stations and surface temperature derived from remote sensing remains unclear. Wang et al. [122] investigated the relationship of canopy UHI (CUHI) and surface UHI (SUHI) using four observations per day, without temporal averaging, in four different cities in two different global regions, with 201 of 2232 CUHI–SUHI pairs exhibiting significant UHI differences in their spatial distributions and intensities. The results indicate that 81.09% of the UHI differences occurred during the daytime and were caused by local air advection related to wind speed ≥2 m/s and land surface conditions

in the study areas. They concluded that a joint analysis of CUHIs and SUHIs should be conducted to characterize urban thermal environments, and that current urban planning procedures should integrate these UHI differences to develop effective mitigation strategies and adaptation measures. The combination of both types of UHI sub-components provides added value for quantifying urban thermal environments, which can assist in developing effective mitigation strategies and adaptation measures. A growing trend is to combine the two methods, both with their own advantages [59].

### *2.6. Develop Controlling Approaches for UHI and UHIRIP*

UHIs occur when cities replace other land covers with dense concentrations of pavement, buildings, and other surfaces that absorb and retain heat. This effect increases energy costs (e.g., for air conditioning), air pollution levels, and heat-related illness and mortality. UHI results from increases in built-up surfaces in urban areas, whereas increasing vegetation cover and water surfaces within cities or urban agglomerations could improve the urban ecological function and thereby improve urban environments for humans [123]. The importance of optimizing urban LULC planning and the development of UHI mitigation methods is increasing. Progress has been made to this end [67,124,125], with the development of UHI mitigating technologies [126]. Ulpiani et al. [127] reviewed an infrared emissivity dynamic switch against overcooling, which is aimed at collecting state-of-theart technologies and techniques to dynamically control the heat transfer to and from the radiative emitter and to ultimately modulate its cooling capacity using spacecraft thermal control, thermal camouflage, and electronics. This work discussed prominent pathways toward technically and economically effective integration in the built environment for UHI and UHIRIP.

### *2.7. UHI and UHIRIP on Socioeconomics and the Urban Ecosystem*

### 2.7.1. Impacts on Human Health

Climate change, increasing urbanization, and an aging population in much of the world are likely to increase the risks to health from UHI, particularly from heat exposure. Additionally, increased urbanization has resulted in a more extensive UHI effects, causing more frequent and intense heat waves in urban regions compared with rural locales [67,128,129]. In urban and surrounding areas, heat waves will be exacerbated by the UHI effect and will have the potential to negatively influence the health and welfare of residents. Heaviside et al. [130] sugges<sup>t</sup> that UHI contributed around 50% of the total heat-related mortality during the 2003 heat wave in the West Midlands of the UK. Moon [131] concluded that the mortality and morbidity risks of diabetic patients under the heat wave were mildly increased by about 18% for mortality and 10% for overall morbidity. Li et al. [132] found that high temperature significantly increases the risk of mortality in the population of Jinan, China. Most research in this topic uses both weather station and in situ measurements in order to investigate the health effects of UHI [129]. Some results [133] show that different sites (city center or surroundings) have experienced different degrees of warming as a result of increasing urbanization [131]. Johnson et al. [134] sugges<sup>t</sup> that thermal remote sensing data can be utilized to improve the understanding of intra-urban variations of risk from extreme heat. The refinement of the current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters. The conclusion is that UHI is directly linked to adverse health effects from exposure to extreme thermal conditions.

### 2.7.2. UHI and UHIRIP on LULC Differences and Change

UHI is a result of continued urbanization, urban agglomeration, and associated increases in paved areas and buildings. Mitigation strategies have been developed to increase vegetation and water surface areas within urban areas to reduce the magnitude of the temperature. One measure of UHI's ecological footprint is estimated by calculating the increase of the cooling demand caused by the heat island over the urban area, and then translating the increased energy use to environmental cost [123,125,135]. Some research shows that the UHI effect has become more prominent in areas of rapid urbanization and in urban agglomerations [136,137]. The spatial distribution of UHI has changed from a mixed pattern, where bare land, semi-bare land, and land under development were warmer than other LULC types, to extensive UHI, as contiguous urbanized blocks grew larger [38,138]. Some analyses showed that the higher temperature in the UHI had a scattered pattern and was related to certain LULC types [97]. In order to analyze the relationship between UHI and LULC changes, some studies attempted to employ a quantitative approach for exploring the relationship between surface temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBI, and temperature are negative when NDVI and NDWI are limited in range, but there is a positive correlation between NDBI and temperature [139–142].

### 2.7.3. Impacts on Regional Economics

Because UHI is related to a significant increase in surface temperature and changes in precipitation patterns, it can potentially affect local economies and the social systems of cities [143]. Some studies [144,145] show that the critical sectors of services, agriculture, and tourism may be strongly affected by future UHIs. To counterbalance the consequences of the increased urban surface temperatures, important research has been carried out resulting in the development of efficient mitigation technologies. In particular, some studies [102,146] have documented the development of highly reflective materials, cool and green roofs, cool pavements, urban greens, water surface, and other mitigation technologies. UHIRIP includes economic impacts, such as increases of energy consumption for cooling purposes, as well as an increase in the peak electricity load, which is a factor for planning maximum power source capacities [147]. Scientists from Australia reported that the total economic cost to the community due to hot weather is estimated to be approximately \$1.8 billion in present value terms. Approximately one-third of these impacts are due to heat waves. Of the total heat impact, the UHI effect contributes approximately \$300 million (AUD) in present value terms for the city of Melbourne, Australia [9]. Estrada, Botzen and Tol [144] provided a quantitative assessment of the economic costs of the joint impacts of local and global climate change for all main cities around the world. They estimated the UHI effect for the 1692 largest cities in the world for the period 1950–2015, and predicted that the percentage of city gross domestic product (GDP) that would be lost for the median city in 2050 due to global climate change alone would be relatively small: 0.9% and 0.7% for the RCP8.5 and RCP4.5 emission scenarios, respectively [144]. At the end of the century, these impacts will increase to 3.9% and 1.2%, respectively. Cost–benefit analyses are presented of urban heat island mitigation options, including green and cool roofs and cool pavements. It has been shown that local actions can be climate risk-reduction instruments. Furthermore, limiting the urban heat island through city adaptation plans can substantially amplify the benefits of international mitigation efforts.
