**1. Introduction**

Urbanization is known to have substantial impacts on landscapes and ecosystems [1–4], and urban inhabitants are expected to reach 70% of the world population by 2050 [5]. Moreover, the nature of urban development has been changing from a single city model to a group of cities (urban agglomeration) worldwide. Urban heat island (UHI), urbanization, and climate change are increasingly interconnected, resulting in several environmental consequences (such as heat stress, biodiversity loss, fire risk, warming water due to run off, and diminished air quality) at both local and regional levels [2,6–9]. Such UHI related impacts are also called UHI regional impacts (UHIRIP). Generally, UHI research includes data from two major sources: air temperature data that are observed by weather or climate stations and remotely sensed data to observe UHI through land surface temperature. Before the availability of remotely sensed data, UHI was widely observed in the field, with the first scientific observation of UHI in 1833 [10]. Field observations of UHI continue to be a critical source of training and validation data [11,12]. These observations, along with modeling studies, continue to help unravel the factors that are responsible for UHI development, and are providing a basis for the development and application of sustainable adaptation strategies. Communicating scientific knowledge quickly and effectively of UHI and UHIRIP to architects, engineers, scientists, and planners could help inform urban

**Citation:** Shi, H.; Xian, G.; Auch, R.; Gallo, K.; Zhou, Q. Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology. *Land* **2021**, *10*, 867. https://doi.org/10.3390/land10080867

Academic Editors: Sara Venafra, Carmine Serio and Guido Masiello

Received: 17 July 2021 Accepted: 11 August 2021 Published: 18 August 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

design and decision making. Remotely sensed data have been used to observe UHI and UHIRIP on environments, ecosystems, human health, and economics in urban and nonurban areas for decades. Remote sensing offers the benefits of long data archives, repeated observations, efficiency, and multiple temporal and spatial resolutions. UHI studies using remotely sensed data have been published for hundreds of cities worldwide [6,7,13–19]. Remotely sensed data provide highly efficient, long-term, and broad-scale information for assessing UHIRIP. However, studies integrating high spatial resolution imagery (e.g., Landsat at 30 × 30 m and ECOSTRESS at 70 × 70 m) from multiple sensors to evaluate UHI and UHIRIP across a time series have been uncommon. Challenges to such studies include image frequency and calibration, cloud contamination, and the need for large storage and high-performance computing capabilities [20,21]. Early generations of broad-scale UHI assessment using remote sensing often poorly represented the spatial and temporal variance in UHI, especially at the urban and non-urban interface. As the resolution of algorithms and satellite imagery improved and interest in UHIRIP grew, researchers sought better representations of UHI. Initially, this took the form of modifications based on surface physical characteristics such as roughness length, albedo, thermal conductivity, and thermal diffusivity [22,23]. Many studies have been conducted to understand the urban thermal climate or the potential for heat island mitigation using this framework of simplified algorithms [24–26]. In more recent efforts, researchers have incorporated more sophisticated parameterization schemes that have included distributions of demography, policies, and behavior of government; ecological variables and ecosystem services; land use and land cover change (LULCC) patterns; and social and economic factors to represent the complicated impacts of UHI [27–36].

Historically, the study of UHI using remote sensing data, often Landsat data, was mainly based on comparing images at two different times using the bitemporal approach [37–39]. Although the bitemporal approach is mathematically simple and does not need large amounts of data, it is less useful than a time series approach that is able to provide a more comprehensive understanding of the complexity of UHI. Most early research [17,40–42] in UHI focused on cities or urban areas, and often ignored the urban and non-urban interface at regional scales. In recent decades, the cost of data storage has dramatically decreased, and we have witnessed an overwhelming increase in computing power and open source software that provide the foundations for time series analysis using higher resolution thermal data from satellite archives. Some studies used Landsat time series to detect historical changes [20,43–46], but few have focused on UHI and its interaction with land use and land cover (LULC) dynamics. A research team at the USGS Earth Resources Observation and Science (EROS) Center recently developed the Land Change Monitoring, Assessment, and Projection (LCMAP) project [47], which is produced with Landsat Analysis Ready Data (ARD) [48] and land surface temperature (LST) data. LCMAP data provide the potential to use Landsat LST data to analyze UHI in urban agglomerations, as well as the urban and non-urban interface at local, regional, and global scales.

This paper reviews remote sensing thermal data sources and the most up-to-date methods used for UHI and UHIRIP investigations. We start by defining UHI, UHII (UHI intensity), regional impacts, urban and non-urban interface, and remotely sensed data sources for LST. We then describe the major distinct approaches that have been used to estimate the magnitude, spatial distribution, intensity, and change pattern of UHIRIP in urban agglomerations and at different urban and non-urban interfaces. Our primary goals in this review are to describe (1) a brief historical summary in the research of UHI and UHIRIP, (2) major thermal data sources and methods used in UHI and UHIRIP research, (3) algorithms used in UHI and UHIRIP analysis, and (4) future research perspective and potential direction. Following the introduction, we discuss the development of UHI and UHIRIP in Section 2; in Section 3, we focus on the application of the remotely sensed thermal datasets in UHI and UHIRIP; we review the algorithms for UHI and UHIRIP in urban and non-urban interface studies based on remotely sensed data in Section 4; in

Section 5, we summarize UHI and UHIRIP based on remotely sensed data; and in Section 6, future research directions are discussed.

### **2. Development of UHI and UHIRIP Analysis**

Most satellite-based investigations of UHIs can be summarized into five main objectives: (1) to examine the spatial features of urban thermal patterns and change dynamics and their relations to urban surface characteristics; (2) to study urban surface energy balances through coupling with urban climate models, including simulation and projection; (3) to study the relations between atmospheric heat islands and surface UHIs through combining coincident remote and ground-based observations; (4) to develop approaches to reduce the magnitude of the UHI and its regional impacts; and (5) to study the UHI effects on ecosystem security at a regional level. Several important reviews, bibliographies, and summaries on UHIRIP using remotely sensed data have been published (see list and descriptions in Table 1). These reviews have concentrated mostly on the various worldwide perspectives of UHI, including the definition of fundamental concepts, summary of methods, applications, exploration of output characteristics, outlines of key research findings, and potential future directions (Tables 2 and 3). The focus of this paper is on the algorithms and methods used in studies employing remote sensing thermal data for UHI and UHIRIP investigation, and future directions in this realm. We summarize (1) the disadvantages of using limited time remotely sensed data for UHI and UHIRIP analysis; (2) the limitations of data shortages due to cloud cover and satellite revisit intervals; (3) the applications of gap filling, data fusion, and deep learning; and (4) the trade-offs between high temporal frequency data (MODIS) and high spatial resolution (Landsat) time series.

**Table 1.** Example of main reviews, bibliographies, and summaries on UHI and UHIRIP using remotely sensed data.



**Table 1.** *Cont.*

**Table 2.** Examples of research publications investigating UHI and UHIRIP using remotely sensed data.


**Table 3.** The temporal frequency and spatial resolution of the main remotely sensed thermal data.

