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The relationships between urban-rural temperature difference and vegetation in eight cities of the Great Plains

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Front. Earth Sci.
https://doi.org/10.1007/s11707-018-0729-5

RESEARCH ARTICLE

The relationships between urban-rural temperature
difference and vegetation in eight cities of the Great Plains
Yaoping CUI1,2, Xiangming XIAO (✉)2,3, Russell B DOUGHTY2, Yaochen QIN1, Sujie LIU1, Nan LI1,
Guosong ZHAO4, Jinwei DONG (✉)4

1 Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China
2 Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
3 Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering,
Institute of Biodiversity Science, Fudan University, Shanghai 200438, China
4 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Abstract Interpreting the relationship between urban
heat island (UHI) and urban vegetation is a basis for
understanding the impacts of underlying surfaces on UHI.
The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas.
Due to the limited number of paired meteorological
stations, many studies have used remotely sensed land
surface temperature, but these time-series land surface
temperature data are often heavily affected by cloud cover
and other factors. These factors, together with the
algorithm for inversion of land surface temperature, lead
to accuracy problems in detecting the UHII, especially in
cities with weak UHII. Based on meteorological observations from the Oklahoma Mesonet, a world-class network,
we quantified the UHII and trends in eight cities of the
Great Plains, USA, where data from at least one pair of
urban and rural meteorological stations were available. We
examined the changes and variability in urban temperature,
UHII, vegetation condition (as measured by enhanced
vegetation index, EVI), and evapotranspirat; ion (ET). We
found that both UHI and urban cold islands (UCI) occurred
among the eight cities during 2000–2014 (as measured by
impervious surface area). Unlike what is generally
considered, UHII in only three cities significantly
decreased as EVI and ET increased (p < 0.1), indicating
that the UHI or UCI cannot be completely explained
simply from the perspective of the underlying surface.
Increased vegetative cover (signaled by EVI) can increase
ET, and thereby effectively mitigate the UHI. Each study
station clearly showed that the underlying surface or
vegetation affects urban-rural temperature, and that these
Received February 13, 2018; accepted July 24, 2018
E-mails: xiangming.xiao@ou.edu (Xiangming XIAO), dongjw@igsnrr.
ac.cn (Jinwei DONG)

factors should be considered during analysis of the UHI
effect over time.
Keywords urbanization, evapotranspiration, urban cold
island, background climate, air temperature

1

Introduction

Urbanization alters Earth’s terrestrial land surface, which
can interact with regional climate (Grimm et al., 2008;
Argüeso et al., 2014; Cui et al., 2015). The urban heat
island (UHI) effect is a typical climate phenomenon
whereby higher temperatures occur in urban areas than in
surrounding rural areas. Due to global warming and
urbanization, urban temperature is dually impacted by
increases of regional background temperature and local
human activities (Zhao et al., 2014). Urban areas have
become ideal natural laboratories for studies on future
climate effects due to the elevated temperatures where
urban areas experience the UHI effect (Zhao et al., 2016).
The impacts of urbanization on temperature remains
inconsistent because of various action mechanisms of
different urbanization factors (Zhao et al., 2014). Based on
current knowledge, the increased urban temperatures are
caused by human activities, such as population aggregation, increased number of buildings, socio-economic
development, and industrialization (Cui et al., 2016).
Previous studies mostly characterized urban heat island
intensity (UHII) and its driving factors in different cities,
but the direct comparison of the UHII among different
cities in different periods did not provide actual insight into
or analysis of UHI mechanisms (Zhou et al., 2013; Cui
et al., 2016; Luo and Peng, 2016). Some studies focused on
analyzing the impacts of urban temperature on global

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Front. Earth Sci.

warming (Kalnay and Cai, 2003; Peterson, 2003; Ren
et al., 2008), but the contribution magnitude of UHI on
global warming is currently a controversial issue (Fang et
al., 2018). Although urban areas are undoubtedly warmer
than surrounding rural areas in many cities, several studies
concluded that the temperature trends in urban and rural
areas were similar and the change of urban temperature
was not significantly different from rural temperature
(Peterson, 2003; Brohan et al., 2006; Jones et al., 2008).
But a few other studies showed an opposite viewpoint
(Kalnay and Cai, 2003; Ren et al., 2008; Piao et al., 2013).
Furthermore, it seems that the urbanization effect on
temperature can be easily understood when we analyze the
relationship between temperature (both land surface
temperature (LST) and air temperature), the expansion of
urban impervious surfaces (Zhou et al., 2013, 2016), and
other urbanization factors, such as population, urban size,
urban landscape configuration, etc. (Clinton and Gong,
2013; Cui et al., 2016; Kamarianakis et al., 2017).
However, when researchers compare the time series
changes of urban temperature with rural temperature,
there is no significant evidence that urban temperature is
high enough to affect regional and global temperature
(Peterson, 2003).
It is still a challenge to clarify the intrinsic impacts of
underlying surface change on the UHI effect due to the
complex factors and mechanisms. For now, the only
conclusion confirmed is the warming effect of urban
expansion (Zhou et al., 2013, 2016), but the reason is
multi-factorial. Some studies concluded that UHI effect
was primarily because of heat-storing structures, which
increased the heat capacity in urban areas (Price, 1979;
Roman et al., 2016). Some researchers believed that
human activity and anthropogenic heat were the major
contributors to UHI (Kusaka and Kimura, 2004; Argüeso
et al., 2014). Increases in impervious surfaces and
decreases in vegetative cover were also identified as
factors that can increase urban temperatures by inhibiting
cooling induced by evapotranspiration (ET) (Lynn et al.,
2009; Imhoff et al., 2010; Li et al., 2011; Dong et al.,
2015). However, following this logic, we cannot completely explain why the urban cold island (UCI, defined as
the urban temperature below the rural temperature around
an urban area) also appears in some cities (Yang et al.,
2017). In other words, urbanization tends to be a linear,
continuous process but urban temperature and UHII
always fluctuates. In fact, even the UCI effect has not a
physical basis (Parker, 2010). Stewart and Oke (2012)
were aware of the conundrum and began to do research in
an area near a meteorological station. A “local climate
zone” classification method was developed by analyzing
the actual land cover types of surrounding local field
stations (Stewart and Oke, 2012). The mesoscale network
of environmental monitoring stations (Mesonet) in Oklahoma (Brock et al., 1995; McPherson et al., 2007) also

considers the local underlying surface types within a
station’s radius (within 5 km) to examine the underlying
surface factors contributing to climate for that station.
When the impact of underlying surface on urban
temperature is emphasized, urbanization can be conceptually divided into two opposing categories, namely a
warming effect (urban expansion and intensification (UEI),
human activities, etc.) and a cooling effect (vegetation
expansion and intensification (VEI), wetlands and open
water bodies expansion, etc.) (Hu et al., 2017). Prior
studies on UHI effects and urban expansion always
focused on dense impervious surface (including streets
networks, building with concrete and steel) and sparse
vegetation (Cui et al., 2012b; Guo et al., 2015). But after
the elimination of natural vegetation and croplands for
urban development, vegetation is often re-planted, fertilized, manicured, and irrigated (Li et al., 2011). That means
during the urbanization process, UEI results in the
increasing of impervious surface area, but urban vegetation
may become well managed at the same time (Golubiewski,
2006). Moreover, with elevated temperature and atmospheric CO2 concentration, the urban environment is
suitable for vegetation growth (Walker et al., 2015; Zhao
et al., 2016). Here UEI can be defined as the increasing of
impervious surface areas, and the VEI can be defined as the
comprehensive impacts of urban environment on vegetation. It has been confirmed that both urban impervious
surface area and vegetation have a strong relationship with
urban and rural temperature (Imhoff et al., 2010; Li et al.,
2011; Cui et al., 2012a; Dong et al., 2015). Currently,
many studies used normalized difference vegetation index
(NDVI) as the indicator of vegetation abundance to
analyze the relationship between vegetation and LST
(Weng et al., 2004; Yao et al., 2018), while the other
important vegetation index, enhanced vegetation (EVI)
that can directly reflect the leaf information of vegetation is
not fully used. Furthermore, although the UHI phenomenon was originally proposed using air temperature (Oke,
1973), remote sensing data is also widely used to
investigate surface UHI (Weng et al., 2004; Li et al.,
2017; Yao et al., 2018). Unlike the atmospheric temperature data observed at meteorological stations, remotely
sensed LST data are often heavily affected by cloud,
aerosols, water vapor, and other factors. Currently, mature
algorithms for inversion of LST all face the inherent
problem of optical remote sensing (Wan, 2008; Mildrexler
et al., 2011), which will directly lead to vacancy and
accuracy problems in detecting UHII, especially relatively
weak UHII as the UHII and its dynamics may be less than
the error range of the LST data itself. Therefore, a reliable
observation dataset should be preferred to analyze UHII
and its temporal changes. In addition, different from the
clear effect of impervious surface, the role of vegetation is
still somewhat uncertain (Weng et al., 2004). Among the
various impact factors, the knowledge about the impact of

Yaoping CUI et al. Relationships between urban temperature and vegetation

vegetation on UHI during urbanization is still insufficient.
Therefore, the relationship between vegetation and air
temperature still need further exploration, especially at
local scale (Dong et al., 2015; Mao et al., 2016).
In this study, we investigated the trends of urban and
rural air temperature in the Great Plains region, in which
urban vegetation usually depends on human management
and the VEI and ET can clearly reflect the condition of
vegetation. The study stations were in Oklahoma, USA,
where the world-class Oklahoma Mesonet stations provide
sufficient and accurate datasets for comparison of urban
and rural stations. The main objectives of this study are to
quantify the urban-rural temperature difference in various
cities and to fully analyze and discuss the impacts of
vegetation on urban-rural temperature difference in the
dual context of local-scale urbanization and regional-scale
atmospheric changes.

2

Materials and methods

2.1

Study area

The State of Oklahoma is in the south-central part of
United States of America. The boundaries encompass
177.8103 km2 of land and 3.2103 km2 of water. The
land is flat and almost all the state belongs to the Great
Plains. Annual average precipitation in Oklahoma declines
from east to west, and its climate mainly belongs to the
cold semi-arid and sub-humid climate types (Kottek et al.,

3

2006). In the southeastern region of the state, annual
average temperature and precipitation can reach 17.0°C
and 1020.0 mm, while in the northwest region, annual
average temperature and precipitation are 14.0°C and 430
mm, respectively. Oklahoma contains 77 counties and
more than 65% of Oklahomans live within metropolitan
areas, or spheres of economic and social impact areas (Fig.
1). Grassland, farmland, and forests are three common land
use and cover types in Oklahoma. Most of the urban areas
are in the Cross Timbers and grassland ecoregions, except
McAlester and Tahlequah. As for urban trees, Oklahoma
Forestry Services recommends planting oak, maple, pine,
redbud, soapberry, crabapple, and cypress.
2.2
2.2.1

Study data and data processing
Mesonet meteorological stations and its impact areas

The Oklahoma Mesonet is a world-class network of
environmental monitoring stations which were designed
and implemented by the University of Oklahoma and
Oklahoma State University. The network has 121 stations
covering the entire state, with at least 1 station in each
county (Fig. 1). All the data are recorded automatically by
instruments mounted on a tower ~10-meters in height, and
air temperature is measured at the standard height of 1.5 m.
The daily data from 2000 to 2014 is summarized and
produced by Mesonet project partners. We averaged the
daily air temperature data to calculate annual mean
temperature. We also converted the original temperature

Fig. 1 Study area and stations. The red and cyan dots are the meteorological stations located in urban and rural areas, respectively. The
red and cyan circles are 5 km buffer areas of each station. The red regions show the impervious surface areas in 2011.

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Front. Earth Sci.

(T) in Fahrenheit (°F) to Celsius (°C) by the formula:


T ð°CÞ ¼ T ð°FÞ – 32  5=9:

temperature minus rural temperature” to represent the
UHII (positive value) and UCII (negative value).

The first step of data processing was to select and pair
urban and rural Mesonet stations. An urban station selected
must be located in an impervious area or in an urban area
surrounded by impervious areas. And the corresponding
rural stations were selected using considering altitude and
distance from the urban station. The nearest station with
altitude difference of less than 100 m to an urban station
was the paired rural station. In total, eight cities with 9
urban stations and 12 rural stations were chosen (Table 1).
Oklahoma City was the only city in which two urban
stations were chosen, each of which was paired with two
rural stations. All other urban stations were paired with 1
rural station, except for Stillwater.
Finally, to define the observational footprint of a station,
we followed the station information provided by Mesonet
and set a 5 km buffer around each station to analyze the
relationship between underlying surfaces (Zhang et al.,
2004b; Cui et al., 2016) and air temperature. These buffers
capture the main observational footprint of each station,
the underlying surface of which have key impacts on air
temperature. Among them, only the buffer of CHIC
overlaps slightly with the buffer of NINN. All eight cities
are in the US Great Plains, which provides a good test area
to focus on analyzing the impact of urban development
within 5 km buffer of a station on temperature and reduces
the impacts caused by other factors, such as greenhouse
gas, etc.
Some Mesonet stations (e.g., OKCE and NRMN) did
not have consistent data from 2000–2014. Therefore, this
study only analyzed corresponding interannual variations
of temperature and vegetation for stations with continuous
observational data. We used the difference value of “urban

2.2.2

Urban impervious surface data

Using the percentage of developed (imperviousness) land
data (National Land Cover Database NLCD) from
America Multi-Resolution Land Characteristics (MRLC)
consortium in 2001 and 2011, we analyzed the UEI within
the 5 km buffer areas of the 21 Mesonet stations from 2001
to 2011. The data can be directly downloaded from MRLC
consortium’s official website.
2.2.3

Vegetation index and evapotranspiration data

We used MODIS products to analyze vegetation variation
within the 5 km buffer areas of each meteorological station.
The Enhanced Vegetation Index (MOD13A3, EVI) with a
spatial resolution of 1 km was used to observe greenness of
vegetation, and we evaluated the relationship between EVI
and air temperature. The EVI data minimizes canopy
background variations and improved sensitivity over high
vegetation conditions. MODIS ET datasets (MOD16A2)
with a spatial resolution of 1 km were used to evaluate the
impact of ET on air temperature. The ET algorithm is based
on the Penman-Monteith equation. The ET data includes
evaporation from wet and moist soil, from rain water
intercepted by the canopy before it reaches the ground, and
the transpiration through stomata on plant leaves and stems
(Mu et al., 2007). It should be noted that some ET grid cells
in urban areas have a null value. In this study, very few grid
cells with null values in the buffer areas of individual urban
stations were considered as purely impervious surfaces and
were assigned as a value of 0. The EVI and ET datasets can
be accessed from Land Processes Distributed Active
Archive Center (LP DAAC, https://lpdaac.usgs.gov/

Table 1 The basic station information within 5 km at the eight cities. All the station information comes from Oklahoma Mesonet
City

Urban station

Rural station

Name

Altitude/m

Major climate regulations

Name

Altitude/m

Major climate regulations

OKCN

362

Grasslands/Herbaceous

SPEN

373

Grasslands/Herbaceous

ELRE

419

Grasslands/Herbaceous

OKCE

355

Grasslands/Herbaceous

GUTH

330

Grasslands/Herbaceous

MINC

430

Grasslands/Herbaceous

BIXB

184

Cultivated Crop

HECT

243

Pasture/Hay

Norman

NRMN

357

Grasslands/Herbaceous

WASH

345

Pasture/Hay

Stillwater

STIL

272

Grasslands/Herbaceous

MARE
PERK

327
292

Grasslands/Herbaceous
Cultivated Crop

Oklahoma City

Tulsa

Tahlequah

TAHL

290

Pasture/Hay

COOK

299

Pasture/Hay

McAlester

MCAL

230

Grasslands/Herbaceous

STUA

256

Pasture/Hay

Chickasha

CHIC

328

Cultivated Crop

NINN

356

Developed Low Intensity

Pauls Valley

PAUL

291

Developed Open Space

BYAR

345

Pasture/Hay

Yaoping CUI et al. Relationships between urban temperature and vegetation

data_access/). EVI and ET data were also averaged to
obtain annual mean values.

3

Results

3.1

Inter-annual variations of urban and rural temperature

Both UHI and UCI effects coexisted in the eight cities. As
the most populous city in the state, Oklahoma City had the
largest UHII with a value of 0.58°C. Conversely, the urban
temperatures in Tulsa, Tahlequah, and Chickasha were less
than rural temperatures, showing an UCI effect (Fig. 2).
Surprisingly, as the second most populous city, Tulsa had
the largest UCI intensity (UCII), but the value of UCII
increased over time, with an average annual rate of –0.38
during 2000–2014.
Furthermore, we used the linear trend method to check
temperature and UHII variations over time. The results
showed that the temperature recorded by four urban areas
and four rural areas decreased from 2000 to 2014 (Table 2).
The remaining four urban stations recorded a slight
increasing trend for the past 15 years. Although the UCI
still appeared in some cities, almost all these cities showed
that the UHII increased slightly (or UCII decreased) with a
mean trend of 0.0003°C$yr–1. In a direct comparison of the
trends of urban temperature with rural temperature, we
found that the temperature trends in rural areas were
always less than that in urban trends except McAlester and
Oklahoma City. Additionally, the maximum and minimum
UHII trends appeared in Tulsa and Oklahoma City,
respectively. Correspondingly, Tulsa had the maximum
urban expansion ratio, while Oklahoma City had the
second minimum urban expansion ratio, implying that the
other factors still play a role of affecting urban temperature.

5

3.2 The relationships between UHII and background
temperature

Urban and rural temperature changes are highly synergistic. Figure 2 showed a significant relationship between
urban temperature and rural temperature (p < 0.1) very
likely because of the likeness of regional temperature and
spatial autocorrelation. However, the differences of interannual variation between urban temperature and rural
temperature still existed, showing that the impact of UEI to
a certain extent.
To further check the relationship between regional
temperature and urban temperature, we used linear
regression of rural temperature versus the UHII. Here we
observed the scatterplot of rural and UHII trends in the
eight cities (Fig. 3). It was clear that UHII or UCII
obviously decreased along with the rural warming in six
cities (failing to pass significance test of 0.1 but passing
significance test of 0.5). It seems that the difference value
between urban and rural temperature tended to lessen over
time under a warming background (signaled by rural
temperature). However, a decreasing trend was not
observed in Norman or McAlester (Fig. 3). Moreover,
the negative relationship between background temperature
and UHII indicated that the variation of UHII was very
different than urban temperature under the background
climate conditions (rural temperature).
3.3 The relationships between vegetation and urban temperature and UHII

Averaged impervious surface areas in buffers of urban
stations continuously increased from 11.13% in 2001 to
13.06% in 2011, while the averaged impervious surface
areas in buffers of rural stations marginally increased from

Fig. 2 The relationship between urban temperature and rural temperature at the eight cities. Blue dotted lines indicate the linear fitting
lines over time; black lines mean the “y = x” lines.

0.021

–0.060

0.009

–0.007

–0.032

0.018

Tulsa

Norman

–0.006

–0.010

–0.028

–0.004

0.001

McAlester

Chickasha

Pauls Valley

Fig. 3

0.001

0.011

–0.001

–0.001

0.000
0.001

0.000

0.000

–0.001

–0.001

–0.001

0.000

0.000

0.000

0.000

0.002

Rural

–0.001

0.003

Urban

EVI trend

–0.002

–0.001

0.000

0.001

0.000

–0.001

0.000

0.000

DEVI

–4.212

–1.410

–1.971

–2.718

–2.635

–3.232

–0.719

–4.149

Urban

–0.647

–1.223

–3.582

1.143

–2.393

–1.406

–1.649

–3.106

Rural

–3.565

–0.187

1.610

–3.860

–0.242

–1.826

0.930

–1.043

DET

ET trend/(mm$yr–1)

4.49

8.66

3.81

0.86

12.58

24.26

5.27

29.07

Urban 2001

0.43

1.25

0.26

0.12

0.98

0.15

0.72

1.76

Rural 2001

5.07

9.50

4.80

1.09

14.54

29.21

8.01

32.26

Urban 2011

Impervious surface percentage/%*

The relationship between temperature trends of UHII and rural temperature in the eight cities from 2000 to 2014.

0.000

0.006

–0.021

0.005

0.004

–0.004

0.009

0.007

Stillwater

0.001

0.028

–0.028

UHII

Tahlequah

Oklahoma City

Rural

Temperature trend/(°C$yr–1)

Urban

City

2001 and 2011

0.44

1.33

0.33

0.12

1.12

0.15

0.76

1.85

Rural 2011

trend difference between EVI or ET for urban and rural areas (EVIurban – EVIrural; ETurban – ETrural). All the value is the trends over time (year) accordingly except the urban impervious percentage (*) in

Table 2 The annual linear trends (slope) of temperature, EVI, and ET from 2000 to 2014, and percentage of impervious surface for urban and rural stations in 2001 and 2011. DEVI and DET is the

6
Front. Earth Sci.

Yaoping CUI et al. Relationships between urban temperature and vegetation

0.71% in 2001 to 0.76% in 2011 (Table 2). Both the
proportion of the original impervious surface areas and
their increase ratios in buffers of urban stations are larger
than that in buffers of rural stations. Correspondingly, the
pervious surface areas, which mainly were composed of
vegetation, in urban areas were always less than that in
rural areas. However, urban temperature did not follow
only the variation of urban expansion but had comparatively independent variability due to the compounding
effects of vegetation or others urbanization factors
(including urban expansion). To examine the impacts of
VEI on temperature variation, we compared the trends of
urban temperature and UHII with EVI in the eight cities.
The trends of urban EVI in the 8 cities were very small
and no trends were observed in Tulsa, Tahlequah, and
Chickasha (Table 2). The trends in EVI for Stillwater,
McAlester, Pauls Valley, and Norman slightly decreased
with the value of –0.001. Oklahoma City showed an
increasing trend, indicating that it was possible to keep a
stable EVI despite increases in UEI. We also chose DEVI
(the difference in EVI between urban and rural areas) to

7

explore the impact of vegetation on UHII or UCII (Fig. 4,
lower panel). The range of DEVI trend in the eight cities
was from –0.002 to 0.001 (Table 2). However, only five
cites showed a negative relationship between DEVI and
UHII and the only statistically significant relationships
(p < 0.1) seen were for Tulsa and Tahlequah (Fig. 4, lower
panel).
To check the cooling effect of vegetation on urban
temperature and UHII or UCII, we used ET and DET to
explore the impact of ET on temperature. Figure 5 showed
that urban ET was less than rural ET for most cities. The
negative DET trends illustrated the increased impact of
extending impervious surface areas on urban ET (Table 2).
Furthermore, the simple linear regression analysis clearly
showed that both ET and EVI had negative correlation with
urban temperature (Figs. 4 and 5). More specifically, the
statistically significant relationship (p < 0.1) between
urban temperature and urban ET for six cities showed
the effective cooling effect of urban ET on urban
temperature (Fig. 5, upper panel). In terms of UHII, the
relationship between DEVI and UHII showed that DEVI

Fig. 4 The relationship between annual mean EVI and urban temperature, and between DEVI (EVIurban – EVIrural) and UHII or UCII in
the eight cities from 2000 to 2014.

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Front. Earth Sci.

cannot be used to interpret the dynamics of UHII due to
other complex variables in each city (Fig. 4). However,
DET still showed an obviously negative correlation in five
cities (Fig. 5, lower panel). It further suggests a direct
cooling effect of ET.

4

Discussion

4.1

Station location and station temperature

Analysis of the impact of urbanization on a changing
climate at the global or regional scale is difficult (Peterson,
2003; Jones et al., 2008; Liu et al., 2014). However, human
activities are certainly concentrated in urban areas. If we
consider the horizontal impacts of other background
variables, like CO2 concentration, atmospheric circulation,
etc., then the impact of urbanization should not to be
neglected (Churkina, 2016; Zhou et al., 2016). We cannot
totally distinguish the effect caused by urbanization and
other background changes in urban areas because
meteorological stations at a certain distance from urban
areas are likely to suffer the same impacts of both
urbanization and other non-urbanization factors. Some of
the urbanization factors that trigger warming in parts of an
urban area are also present in rural areas (Peterson, 2003).
But the impacts of background changes should be
excluded as much as possible so that we can focus on
the impact of underlying surface change on temperature.
There are more open areas in small and medium-sized
cities than megacities. We assume these eight cities receive
similar impacts as do urban and rural areas, and they can
effectively avoid some of the complex impacts caused by
horizontal turbulence (Bang et al., 2010; Hutyra et al.,
2014; Haashemi et al., 2016).
Land surface interacts with air temperature, especially
within the lower few thousand meters of the atmosphere,
thereby influencing local and regional climatic properties
(Chandler, 1976). At the local scale, the underlying surface
surrounding a meteorological station has a major impact on
the observed temperature (Stewart and Oke, 2012; Hu et
al., 2017). In this study, all the instruments used to measure
temperature are fixed at 1.5 m above ground on Mesonet
towers. The footprint of 1.5 m sensors may be a few tens of
meters in neutral conditions, smaller when atmosphere
conditions are unstable, and larger when it is stable
(Schmid et al., 1991; Oke, 2004; Stewart, 2011). Therefore, the land surface at local scale plays a vital role for
in situ temperature, especially in small cities (Piao et al.,
2013; Zhao et al., 2014; Hu et al., 2017). However, the
relationship between station and their representative areas
(point and polygon) is an issue that has always existed in
remote sensing research. The introduction of “footprint”
provides a way to quantify the source areas of a station, but
the footprint is variable over space and time. At the
interannual scale, due to the impacts of many meteor-

ological variables such as prevailing winds etc., it is
difficult to know the exact size and shape of the footprint at
any given time. The rule we can infer may be that the
further the distance away a station, the poorer the
representativeness of a station. In this study, we directly
used 5 km as an impact radius of a station based on others’
study (Liu et al., 2011). When Mesonet provided the
observation data, they also provided an introduction
document containing the surface cover conditions
(named as major climate regulations) within 5 km areas
of all the stations (Table 1). It is true that 5 km does not
consider the footprint of a field station. This is a deficiency
and assumption of this study.
Our results show that UHI and UCI coexist in the eight
cities, although urban stations usually experience more
warming effects due to urbanization (Fig. 2). The
maximum impervious surface is less than 35% (Table 2),
and the corresponding vegetation area is always more than
impervious surface within a buffer, showing that as the
important factors of underlying surface, impervious surface and vegetation together regulated the result of UHI or
UCI in a city at the local station (Stewart and Oke, 2012;
Heusinkveld et al., 2014). It seems that regional conditions
most directly impact the overall temperature values of both
urban and rural stations, but the local underlying surface
decides the specific temperature variation of a station
(Chandler, 1976; Stewart and Oke, 2012; Zhao et al., 2014;
Hu et al., 2017).
4.2 The trend differences between urban and rural temperature

The most reliable way to investigate possible temperature
biases affected by urbanization is to compare rural and
urban station data over time (Brohan et al., 2006). As
mentioned in previous sections, some high or low
temperatures within a city cannot be captured due to the
relatively small footprint of the urban Mesonet stations.
Indeed, LST data has been widely used to research the UHI
effect (Peng et al., 2012; Bokaie et al., 2016; Cui et al.,
2016; Jenerette et al., 2016; Liu et al., 2016). In contrast to
LST data, air temperature can be perceived directly by
individuals. In addition, LST data has more null grids with
unfixed location due to the impact of clouds and shadows.
It is difficult to do a long-time series analysis (Hu and
Brunsell, 2013). Maybe such an analysis can be conducted
after interpolation, but the precision is hard to guarantee,
especially when the UHI signals are weak in small and
medium-sized cities.
With the observed air temperature, our results clearly
show that UHII or UCII is small in the eight small and
medium-sized cities. Even in Oklahoma City, the annual
UHII is 0.58°C. Previous studies demonstrated that the
daily temperature within urban core was 0.5°C and nightly
temperature was 2.0°C warmer than rural areas during an
intense heat wave period (Basara et al., 2010). The trends

Yaoping CUI et al. Relationships between urban temperature and vegetation

of urban and rural temperature are also relatively small
(0.0003°C, Table 2) but different in the eight cities. Our
results were similar with some previously published results
(Peterson, 2003; Jones et al., 2008). However, the similar
trends may be due to the varying proximity of the urban
and rural stations we paired for this study, it cannot be
interpreted that the urbanization has no-significant impact
on background temperature and our findings clearly
indicate that the trend of urban temperature is greater
upward than rural temperature (UHII > 0) in most cities.
Contrary to general expectations, UHII decreases as the
background temperature increases (Fig. 3). The finding
indicates that UHIIs tend to be stable and even decrease
under regional background warming, and it may imply that
the effect of UHII may be reduced in the future. However,
there were inconsistent results for two cities (Norman and
McAlester). Given that the station temperature cannot
reflect the local high temperature often occurring in urban
core, the entire variation of UHII in urban areas needs
further analysis.

4.3

9

The cooling effect of vegetation intensification

The processes of UHII varies with latitude, climate,
topography, and meteorological conditions (Haashemi
et al., 2016). From a perspective of heat energy exchange
and diffusion, temperature can be impacted by vertical and
horizontal thermal circulation. Horizontal impacts mainly
take into account the atmospheric turbulence and transmission within a region (Eliasson, 1996; Cui et al., 2015). It is
very complex and must be simulated by regional climate
models (Cui et al., 2015). Even so, the input variables do
not adequately reflect the indirect impacts of all factors
such as the greenhouse gases, the interruption on light and
wind conditions caused by roadways, human activities and
industry, convection, and circulation (Bang et al., 2010;
Lietzke and Vogt, 2013; Hutyra et al., 2014). As to the
vertical impacts, they mainly come from differences in the
energy budget caused by land surface and anthropogenic
factors (Oke, 2004). The construction of urban areas and
human activities result in a dramatically alteration of land

Fig. 5 The relationship between annual mean ET and urban temperature, and between DET (ETurban – ETrural) and UHII or UCII in the
eight cities from 2000 to 2014.

10

Front. Earth Sci.

surfaces and energy flows in and around urban areas
(Chandler, 1976). Vertical effects contain the impacts of
both UEI and vegetation intensification.
Urban and vegetation expansion and intensification
(UEI/VEI) are two aspects of urbanization and affect the
land surfaces around a meteorological station and within a
buffer (Fig. 6). The process of urbanization, on one hand,
increases impervious surface and decreases vegetation
areas, thereby causing the urban areas to have more
sensible energy than their rural areas (Cui et al., 2012a). On
the other hand, introduced and well-managed urban
vegetation can encourage plant growth and increase EVI,
thereby reducing the urban temperature (Li et al., 2011;
Qiu et al., 2013; Guo et al., 2015; Cui et al., 2017).
Greenspace has been observed to increase in areas
previously covered by impervious surfaces (Qian et al.,
2015). The warming effect of impervious surfaces has been
relatively clear, but the relationship between vegetation
and UHI is complex (Weng et al., 2004). Compared to
vegetation indexes, ET can explain the impact of
vegetation on temperature directly and obviously (Zhao
et al., 2018). During the process of increasing impervious
surfaces, more bare lands are covered by regular and wellmanaged vegetation (Figs. 6(a), 6(b), 6(e), and 6(f)).
Figures 6(c) and 6(d) clearly show that vegetation decline
is delayed and the growth period of vegetation in urban
areas is extended by urbanization and human management,
compared with natural vegetation in rural areas. So, in this
sense, just taking notice of UEI or VEI is unilateral while

the climatic effects of urbanization are bi-lateral. The UEI
and VEI effect on temperature should be the main
regulation factors and drivers to UHI or UCI. Urban
vegetation may experience enhanced growth periods and
carbon uptake since the urban vegetation typically receives
more irrigation, fertilization, higher temperatures, and
longer growth season (Imhoff et al., 2004; Zhang et al.,
2004a; Golubiewski, 2006). Urban landscaping, the most
common greening action in cities, is quite significant in the
aspect of reducing warming effects of urbanization (Watts
et al., 2015). By absorbing heat or shading impervious
surfaces, vegetation plays a substantial role in reducing the
urban temperature (Wong et al., 2003; Steeneveld et al.,
2011; Qiu et al., 2013). In this study, despite the continuous
reduction in total vegetative area in urban areas, the
temporal stability of vegetation greenness measured by
EVI suggested that urban vegetation has been well
managed in all the eight cities and continues to age
(Table 2). For instance, increases in EVI over time can
indicate more complex canopies in addition to increases in
leaf area. Furthermore, vegetation produces a cooling
effect mainly through ET (Zhao et al., 2018), but the
analysis results of ET show that the negative impact of
increased impervious areas may be hard to be offset by
vegetation management in urban areas. No matter the
warming effect driven by urbanization or the cooling effect
induced by vegetation intensification, only one net effect
can be shown in a city. Our results illustrated that
temperature differences between urban and rural areas in

Fig. 6 The urban/vegetation expansion and intensification (UEI/VEI) in an urban area. Human-managed vegetation in urban areas:
(a) introduced plants; (b) irrigation; (c) natural vegetation decline in winter; and (d) urban vegetation growth in winter (Oklahoma,
December 9, 2016), possibly due to irrigation and UHI. The comparison of (e) to (f) demonstrates the coexistence of increases in UEI and
VEI. The two photos were taken in August 2003 and September 2016 and accessed via Google Earth.

Yaoping CUI et al. Relationships between urban temperature and vegetation

six cities of the eight cities had an increasing trend and two
did not (Table 2), suggesting that the cooling effect of VEI
may have offset the warming effect of urbanization.
However, other impact factors, such as background
environment and climate conditions still play an important
role at the local or regional scales. We cannot distinguish or
mask their effect although the Great Plains cities may
receive the equivalent effects in urban and paired rural
areas at times. The factors that drive UHI or UCI thus
needs more studies in future.

5

Conclusions

In this study, we compared and analyzed variations in
temperature for urban and rural Mesonet stations, as well
as the cooling effect of VEI in eight Great Plains cities. We
found that the urban heat or cold island effect was present
in each of the eight cities and underlying surface types play
an essential role in regulating the station temperature. In
addition, we found that UHII decreased as the increasing of
rural temperature in six cities, but more studies should be
scrupulously done to verify preliminary results in the
future.
UEI and VEI affected the urban temperature through
warming and cooling, respectively. Urban expansion,
measured by impervious surfaces, increased over time.
Correspondingly, vegetation intensification, measured by
EVI and ET, mitigated the UHI. Urban vegetation
greenness demonstrated a strong temporal stability, but
evapotranspiration continuously decreased during impervious surface expansion. Overall, urban vegetation played
a great role in cooling urban temperatures. The integrated
result also showed that the warming effect of UEI and other
urbanization factors were dominant compared to the
cooling effect of vegetation intensification. Although
vegetation showed a cooling effect, the other factors still
cannot be ignored for interpreting the UHI phenomenon in
each city. The role of underlying surfaces and vegetation
should not be overestimated in the determination of
differences in urban and rural temperature. However, the
other factors of non-underlying surfaces impacting urban
temperatures needs further study.
Acknowledgements We thank Oklahoma Mesonet, which is designed and
implemented by scientists at the University of Oklahoma (OU) and at
Oklahoma State University (OSU), for providing the meteorological data for
the entire state of Oklahoma. We thank Multi-Resolution Land Characteristics
(MRLC) consortium for providing the percent developed imperviousness
data layer. We thank NASA EOSDIS LP DAAC and the Numerical
Terradynamic Simulation Group for providing the MODIS EVI and ET
datasets. This study is supported in part by research grants from the Strategic
Priority Research Program of the Chinese Academy of Sciences
(XDA19040301), the National Science Foundation EPSCoR program of
American (IIA-1301789), the National Natural Science Foundation of China
(Grant Nos. 41671425 and 41401504), HENU-CPGIS Collaborative Fund
(JOF201701), the Key Research Program of Frontier Sciences by the Chinese

11

Academy of Sciences (QYZDB-SSW-DQC005), and the “Thousand Youth
Talents Plan.”

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