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Influence of 2D/3D urban morphology on diurnal
land surface temperature from the perspective of
functional zones
Qianmin Zhang, Jun Yang, Xinyue Ma, Jiaxing Xin, Jiayi Ren, Wenbo Yu, Xiangming Xiao, and Jianhong Xia
Abstract—Optimizing the spatial distribution of urban
functional zones (UFZs) effectively improves the thermal
environment. This study utilized an enhanced regression tree
model and relied on ECOSTRESS data to analyze the relative
contributions and marginal effects of 2D/3D urban morphological
factors on the diurnal land surface temperature (LST) in
Shenyang, China. The results showed that public and residential
areas dominated Shenyang’s UFZs. The temperature in industrial
areas was the highest during the day, and residential and
commercial functional areas are high temperature concentration
areas. Furthermore, the effects of the urban spatial morphology
on the LST differed between diverse time points and UFZs. The
digital elevation model and normalized difference vegetation index
contributed significantly to daytime and nighttime LSTs.
Construction indicators, such as the normalized difference built-
up index and the proportion of construction land, significantly
impacted commercial services. Residential daytime LST had a
large contribution value, and the sum of its contribution rates
reached approximately 30%. Population greatly contributed to
the nighttime LST of the industrial and residential zones,
accounting for 16.77% and 22.06%, respectively. Vegetation
contributed to the cooling effect on daytime LST in summer,
especially in industrial areas, contributing 29.79%. In addition, 3D
indicators, such as building height and building density,
contributed to diurnal LST. Finally, when the proportion of
construction land reached approximately 45%, it negatively
affected LST. In this study, the main factors affecting day and
night LSTs were identified, and this work acts as a relevant
strategic reference for alleviating the urban heat island effect.
Index Terms—Urban functional zones; Urban morphology;
Diurnal surface temperature; Boosted regression tree
This research study was supported by Liaoning Revitalization Talents
Program XLYC2202024, the Fundamental Research Funds for the Central
Universities (grant no. N2111003), Basic Scientific Research Project (Key
Project) of the Education Department of Liaoning Province (grant no.
LJKZ0964), and the Fundamental Research Funds for the Central Universities
(grant no. N2411001). (Corresponding author: Jun Yang.)
Qianmin Zhang and Xinyue Ma are both from Urban Climate and Human
Settlements Research’ Lab, Jangho Architecture College, Northeastern
University, Shenyang 110169, China (e-mail: 2301575@ stu.neu.edu.cn;
womaxinyue@163.com)
Jiaxing Xin, Jiayi Ren and Wenbo Yu are with the School of humanities and
law, Northeastern University, Shenyang 110169, China (e-mail:
2310012@stu.neu.edu.cn; 2210011@stu.neu.edu.cn;
2110013@stu.neu.edu.cn).
Jun Yang is with Urban Climate and Human Settlements Research’ Lab,
Jangho Architecture College, Northeastern University, Shenyang 110169,
China, and also with Human Settlements Research Center, Liaoning Normal
University, Dalian 116029, China (e-mail: yangjun8@mail.neu.edu.cn).
Xiangming Xiao is with the School of Biological Sciences, University of
Oklahoma, Norman, OK 73019 USA (e-mail: xiangming.xiao@ou.edu).
Jianhong Xia is with the School of Earth and Planetary Sciences (EPS),
Curtin University, Perth 65630, Australia (e-mail: c.xia@curtin.edu.au).
I. INTRODUCTION
n the process of urban expansion and development, human
activities have changed the natural surface of land, and the
impervious areas and the number of building have
increased sharply, exacerbating the heat island effect [1]. The
Urban heat island (UHI) results from urban areas experiencing
elevated temperatures compared to surrounding rural areas, and
this is influenced by alterations in urban surface features and
human activities [2], which contributes to significant issues,
including a deterioration in worsened air quality, heightened
energy usage, and alterations in vegetation phenology [3].
Residents in urban areas experiencing persistent high
temperatures are at an increased risk of developing
cardiovascular and respiratory diseases [4]. Therefore, studying
the driving mechanisms of the UHI effect is crucial for
responding to climate change and achieving resilient urban
development.
Many studies have analyzed the driving mechanisms of the
UHI effect. Liu et al. [5] found that with the acceleration of
urban construction and urbanization, the intensity of UHIs had
also increased, and Beijing had experienced a temperature
increase at a rate of 0.31 °C per decade over the previous 40
years. In addition, Oke [6] categorized the concept of the UHI
into three components: canopy, boundary layer, and land
surface layer. With the rapid development of remote sensing,
land surface temperature (LST) has been widely studied, and it
is known to be intrinsically related to human lives [7], [8], [9].
This is associated with seasons, time, vegetation fraction, water
area, the proportion of impervious areas, and population density
[10], [11], [12], [13], [14].
Satellite remote sensing observations have the advantages of
wide coverage and regular return visits, and have been widely
used to produce different surface temperature products at
multiple scales with different spatial resolutions [15].The
Ecosystem Spaceborne Thermal Radiometer Experiment
(ECOSTRESS), launched on June 29, 2018, provides regular
monitoring of LST data for various times throughout the day
and night with a temporal and spatial resolution of 3–5 days and
70 m, respectively [16], [17]. This data derives LST and
emissivity from five thermal infrared bands, which can be used
to identify the factors influencing diurnal LST changes and
provide time-series data support for UHI mitigation strategies
[18]. Although the use of ECOSTRESS is currently relatively
limited in its studies of the thermal environment [19]. For
instance, Lin et al. [20] used ECOSTRESS data to study the
spatiotemporal connection between urban morphology and day
I
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
2
and night surface temperature under different local climate zone
classification frameworks in Fuzhou City based on
ECOSTRESS data. In addition, some studies have explored the
differences between diurnal and seasonal LSTs in Beijing [21].
LST is intricately linked with urban spatial form (USF)
indicators from both two-dimensional (2D) and three-
dimensional (3D) viewpoints，and some studies have started
to focus on the impact of 3D morphology on LST [22]. Three-
dimensional buildings enhance the absorption of solar radiation
absorption and diminish natural ventilation within cities,
leading to intensified urban heat accumulation [23], [24], [25].
Han et al. [26] found that 3D indicators significantly influenced
surface temperature during the cold seasons, and that building
coverage, the ratio of tall buildings, and the standard deviation
of building heights consistently had the greatest impact.
However, related research needs to focus on how 2D and 3D
morphological indicators affect the diurnal and noctule surface
temperature.
Urban functional zones (UFZs) are the basic units associated
with social and economic activities and resource allocation, and
they are intrinsically related to people’s living needs. They are
affected by natural conditions, social and economic
development levels, policy planning and other factors, which in
turn affect the urban layout [27], [28]. The interaction between
the USF and the thermal environments in different functional
areas can result in ecological problems and affect human health
[29]. Prior studies have primarily investigated the correlation
between the USF and LST across various scales: regional,
urban, block, and grid [30], [31], [32], [33]. For instance, An et
al. [34] examined how urban greenspace characteristics affect
LST at the block scale. Nevertheless, studies have shown that
the contributions of UFZs to LST differ. Several studies have
explored the link between USFs and LST through the lens of
UFZs, revealing variations in the significance of USF on LST
[35], [36]. For instance, Chen et al. [37] found that the LST
within man-made functional areas is higher than that of the
natural area. However, studies have also shown that that the
contributions of UFZs to LST differ.
In addition, most studies have focused on the correlation
between single variables and have ignored the marginal effects
of various parameters on LST. The marginal effect of a variable
refers to the dynamic relationship between the independent
variable and the dependent variable when other variables
remain unchanged when the independent variable increases. In
this respect, uncovering the marginal effect makes it possible to
elucidate the nonlinear relationship between USF and LST, and
ultimately to optimize their configuration to effectively relieve
the urban thermal environment [38]. To achieve this, boosting
regression trees (BRTs) have been employed to effectively and
scientifically derive the marginal effects of each driver and
capture the significance of predictors [39], and they have been
generally applied in urban outward expansion and thermal
environment research. BRTs have been extensively applied in
fields such as urban expansion and the thermal environment
yield due to their ability to effectively extract marginal effects
for each factor and their ability to highlight the significance of
predictors.
To compensate for the limitations of the existing research,
we chose the main urban area of Shenyang to examine factors
affecting LST in various UFZs. Our specific study aims were as
follows:
(1) Exploring the spatial differentiation between daytime and
nighttime LST from the perspective of urban functional blocks.
(2) Employing the BRT model to discover the correlation of
drivers and LST during daytime and nighttime.
(3) Analyzing the marginal effects of urban morphological
variables and the contribution of USF on diurnal LST.
II. MATERIALS
A. Study area
Shenyang, the capital of Liaoning Province, serves as the
central hub of the Shenyang Metropolitan Area (Fig. 1). This
area connects the Bohai Rim with northeast regions. Positioned
between 122°25’9” E, 41°11’51” N and 123°48’24” E,
43°02’13” N, Shenyang has a temperate continental monsoon
climate with distinct seasons. The climate features mild and
windy springs and autumns, hot and rainy summers, and long,
cold winters. The average annual temperature is 8.4°C, with
temperatures ranging from -35°C to 36°C throughout the year.
The average annual precipitation is 672 mm, with the majority
of rainfall occurring between June and September. Prevailing
winds are generally from the west throughout the year, with
southwestern winds dominant in spring and summer. The total
annual sunshine hours and accumulated temperature above
10°C are 2550.7 h and 3488°C, respectively. As an important
industrial base in China, Shenyang has experienced a period of
rapid urbanization and has faced the threat of extreme weather
in recent years. As such, it can be used as a reference for the
development of cities with similar development backgrounds.
The study area is the urban center of Shenyang, including the
Dadong, Heping, Huanggu, Hunnan, Shenbei, Shenhe, Sujiatun,
Tiexi and Yuhong districts.
B. Data sources
TABLE I
DATA DESCRIPTION
This study made use of spatial data from multi-sources
(TABLE I). Specifically, to obtain and analyze analysis-ready
data cases, we utilized ECOSTRESS level 2 products acquired
through NASA’s tool [40]. We chose the LST of the four hottest
months (June–September) in Shenyang as the research object.
We utilized four high-quality images without cloud cover from
August 30, 2023, September 1, 2019, August 6, 2022, and
September 14, 2023. Point-of-interest (POI) data were derived
from the Amap Open Platform [41], [42]. Each POI dataset
provided detailed attribute information (such as longitude and
latitude, name, address, and type, combined with 200 m grid
data) which was used to identify UFZs in the city. Data on
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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Fig. 1. Location of the study area. （a represents Location of the Liaoning Province in China ,b represents Location of the study
area in the Liaoning Province and c represents The digital elevation model (DEM) of the study area）
buildings, including building area and number of floors, were
acquired from the Baidu map service platform and used for
building index calculations [43]. Land-use data from the China
Land Cover Dataset (CLCD) were used to calculate landscape
indicators [44], and population data were obtained from the
seventh national census of 2020 [45].
III. METHODS
A. Identification of Urban Functional Zones
Urban function is determined by external influences and a
city’s internal organizational structure, and identifying the
distribution characteristics of UFZs is important for urban
development and spatial resource allocation [46]. Some studies
have used remote sensing images and nighttime light data to
classify urban land use; however, they have not considered the
socioeconomic attributes of surface objects or the type and
intensity of human activities [47], [48], [49], [50]. With the
widespread application of big data in the socioeconomic and
urban planning fields in recent years, many scholars have
implemented UFZ divisions based on POI, social media, mobile
phone positioning, and public transportation data. Among them,
POI data have a large sample size and rich information,
representing the distribution of functional nodes on the urban
microscale. They can indicate the intensity of human activity
heat sources to realize the division of UFZs [51], [52], [53]. In
this study, we selected a 200 × 200 m grid to identify UFZs
conducive to the expression of similarity. In accordance with
the standards for urban land classification and planning
construction (GB 50137-2011), the POI data were categorized
into six categories: residential, road traffic, commercial service,
public service, industry, and green space.
In identifying UFZs, the frequency density and type ratio of
POIs within a research unit (POI feature vector) are often used
to identify the urban functional arrangement and directly reflect
the urban functional type.
To facilitate comparison between different POI types, the CR
vector was employed to indicate the percentage of each POI
type within a unit based on its density:
In equations (1) and (2), i represents the POI type, and Si
denote the count and the aggregate count of the i-th type of POI
in the research unit, Fi is described as the i-th type of POI
frequency density in the overall count of POIs of this type, and
Ci indicates the frequency density of the i-th type of POI
accounting for the frequency density of all types of POI in the
research unit.
When classifying the type, researchers generally select 50%
as the basis for classification. If the CR vector value is greater
than or equal to 50%, the unit is defined as a single UFZ [54].
TABLE II lists the classification of POI data.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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4
`
Fig. 2. Technical route.
TABLE II
POI-OF-INTEREST DATA CLASSIFICATION
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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TABLE III
URBAN MORPHOLOGICAL FACTORS
B. ECOSTRESS data
The ECOSTRESS project measures the LST of vegetation to
understand water demand. Its application areas have expanded
since its launch, and it plays important roles in the UHI effects,
abnormal weather monitoring, and mineral geological
exploration. Previous studies have indicated strong agreement
between the ECOSTRESS LST product and LST data obtained
from established thermal infrared instruments [55], [56], [57].
The root mean square error (RMSE) at 14 validation points
around the world is 1.07 K, the mean absolute error is 0.40 K,
and R2 > 0.988. This research utilized ECOSTRESS data to
collect the LST for various dates and periods within the study
region. The four study times were 01:32 on August 30, 2023;
09:37 on September 1, 2019; 11:58 on August 6, 2022; and
19:56 on September 14, 2023. The background temperature
ranges of the four dates were 7–23, 13–45, 12–61, and 6–23 °C,
respectively.
C. Urban morphological factors
Studying the effect of 2D/3D urban forms on LST is
important to respond to climate change [58]. The 2D/3D
parameters primarily included land cover, architectural form,
landscape pattern, population, and other related indicators [59].
To exclude the influence of factor collinearity, the variance
inflation factor (VIF) was used for testing, and 14 factors
with VIF<10 were retained for subsequent analysis [60]. The
distribution of urban form factors can be obtained by
calculating the average value of the unit indicators in each
functional area. The implications of each indicator are
presented in TABLE III. Among them, the relevant indicators
of landscape patterns include patch density (PD), the Shannon
diversity index (SHDI), and the contagion index (CONTAG),
which can fully describe structural characteristics and spatial
configuration of landscape [61], [62]. Additionally, the shape
and structure of buildings significantly affect UHIs [63]. In this
study, we selected relevant indicators, such as BH, BD, SVF,
and floor area ratio (FAR) [64], [65]. Population density can
also reflect the socioeconomic status of a region [66]. All the
data were projected, clipped, and unified into 20 m × 20 m
grid cells.
D. Statistical analysis
To investigate the impact of various USF indicators on LST,
we examined the correlation between these indicators and LST
at four different time points. Unlike other analytical methods,
the Spearman coefficient is more suitable for data with non-
normal distributions. Moreover, the BRT is a statistical
machine-learning technique that advances a single model’s
predictive performance by integrating multiple models and
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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6
combining their forecasted outcomes effectively [67]. The core
of this method is its unique decision-tree structure and boosting
algorithm. Compared with other models, the BRT simplifies the
consideration of complex interactions among independent
variables by directly revealing influence of these variables and
response patterns on the dependent variables [68]. Decision
trees are widely favored due to their ability to present
information in a clear and graphical format that is intuitive and
easily understandable for users. In addition, BRT comprises
flexible and diverse predictor variable types, including but not
limited to numerical, binary, and categorical types, which
greatly broadens the scope of its application. The BRT model is
robust to monotonic transformations of predictors or
measurement scales and can automatically screen out predictors
with higher correlations, thus simplifying the screening process
for candidate predictors. Boosting technology, integral to BRT,
enhances prediction accuracy through iterative, staged
processes that progressively improve model performance [69],
[70]. The BRT method generally excels in predicting and
identifying interactions among multiple variables, effectively
assessing the relative contribution and marginal effects of urban
form on LST. It comprehensively evaluates variable influences
[71].
IV. RESULTS
A. Urban functional zone identification results
Fig. 3 illustrates an illustration depicting the segmentation of
UFZs. The results of the confusion matrix show the overall
classification accuracy is 87.1%, and the Kappa coefficient is
0.843, reflecting strong consistency. The urban functional area
recognition model thus has high accuracy and can be used as a
reference for urban spatial structure characteristics.
Commercial, public service and residential POIs were mainly
fixed on the central area; industrial POIs were fixed on the
surrounding areas, mainly in the Tiexi District; transportation
POIs were predominantly clustered along major roads, airports,
Fig. 3. Urban functional zone identification results.
and high-speed rail stations. Greenspace POIs were scattered.
In total, 17,538 valid grids were obtained. Residential, public
service and industrial land proportions were relatively large,
with public service land accounting for the largest proportion
(22.11%) followed by industrial land (21.09%). Greenspace
land accounted for 6.35%, and the overall greenspace
configuration must be strengthened.
Fig. 4. Spatial distribution of diurnal and noctule land surface
temperature (LST) in the study area. (a-d represent the surface
temperature information at 01:32, 09:37, 11:58, and 19:51)
B. Spatial distribution of diurnal surface temperature
TABLE IV displays statistical data of LSTs at various
observation times. Among the four study times, the lowest LST
value appeared at 01:32, with an average LST of 14.5 °C. As
the day progressed, the LST value increased, reaching 26.0 °C
at 09:32, peaking at 36.1 °C at 11:58, and declining at 19:51
after sunset. The average LST was 16.2 °C.
The spatial variation in the daily average LST is shown in
Fig. 4, where Fig.4 (a) and (d) show the LST data obtained at
night. Bodies of water exhibit the highest values. The LST in
built-up areas was relatively high and tended to spread outward.
As shown in Fig. 4(b), the average LST increased by 8 °C at
09:37. LSTs were significantly different at night and the water
body temperature was relatively low. With a rise in the
impermeable surface temperature, LSTs in the western and
southern regions of the study area increased rapidly, whereas
they remained relatively high in the central region. At 11:58,
this trend was further strengthened and the LST was 10 °C
higher than the average temperature in the morning, as shown
in Fig. 4 (c). The LST at 19:51 is shown in Fig. 4(d), where the
high-temperature aggregation phenomenon is evidently
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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7
TABLE IV
STATISTICS OF LAND SURFACE TEMPERATURE ACROSS FOUR OBSERVATION TIMES.
Fig. 5. Box plots of land surface temperature (LST) in different
urban functional zones at four time points. (a-d show the
maximum, minimum, median and other related values of the
surface temperature value in different functional areas at four
times: 01:32, 09:37, 11:58, 19:51.)
alleviated to a certain extent.
The differential changes in the LST among various UFZ
types are shown in Fig. 5. At night, the temperature values of
the commercial and industrial zones were lower, and higher
temperature values were recorded in the green areas. During the
day, the industrial zones quickly heated, reaching 43.1 °C at
11:58. The LST of zones with human activities such as
residential, commercial, and public services were also relatively
high. Green areas became cold island areas during the day, with
mean LSTs of 27.5 °C and 39.8 °C at 09:37 and 11:58,
respectively.
C. Relative contribution of urban 2D/3D morphological
drivers to diurnal LSTs
The effects of different urban morphology elements on LST
during both day and night are exhibited in Fig. 6 and Fig. 7.
BRT regression and Spearman correlation coefficients
identified the direction of effects. If the Spearman correlation
indicates a positive relationship, the overall contribution is
positive, and vice versa. During the daytime, normalized
difference built-up index (NDBI) and the proportion of
construction land (PCL) were positively correlated with the
daytime surface temperature. Two dimensional morphological
factors such as digital elevation model (DEM) and the NDBI,
impacted commercial and industrial zones more. In contrast,
residential zones were affected by 3D building forms such as
BD. PCL was more important for green zones. At night, the
normalized difference vegetation index (NDVI), population
(POP), and other drivers predominantly influenced the LST
across different UFZs within the city, and there was a
significant negative correlation between SVF and night surface
temperature. Furthermore, 3D building indicators such as BH
contributed significantly to residential zones. Building height
and BD were important 3D factors influencing the diurnal and
noctule LST.
At 01:32, the NDVI contributed the most to the commercial,
green, and transportation zones at 14.39%, 15.30%, and 15.28%,
respectively. POP had a greater impact in the industrial and
residential zones (16.77% and 22.06 %, respectively). Building
height was an important 3D factor affecting the public service
zones, reaching 18.29%.
At 09:37, the DEM had the highest impact on UFZs, such as
commerce, green space, and public services, at 16.28%, 20.64%,
and 15.86%, respectively. Compared to 01:32, the importance
of the PCL in the LST increased. Considering the greenspace
UFZ as an example, the contribution increased from 3.61% to
18.83%. The impact of BD on residential areas increased
significantly, from 3.51 to 13.59%. The NDBI has become an
important 2D-factor affecting industrial zones, with a
contribution value of 24.03%, and its impact on other UFZs
increased.
Fig. 6. Relevance between surface temperature and
morphological factors at different time points. (a-d represent
the correlation index between USF factors and LST at 01:32,
09:37, 11:58 and 19:51，p<0.05）
At 11:58, NDBI became the dominant factor affecting
various UFZs, and its importance in commerce, industry, public
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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Fig. 7. Relative contribution of urban morphology factors to daytime and nighttime land surface temperatures.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
9
Fig. 8. Marginal effect of urban morphology drivers on land surface temperature at 01:32.
services, and transportation zones was 16.40%, 29.79%,
19.26%, and 16.32%, respectively. The impact of PCL on
various UFZs increased, and its contribution value to the
greenspace UFZ reached 21.29%.
At 19:51, the impacts of the NDVI and POP recovered.
Among them, NDVI was the largest 2D morphological factor
affecting commercial and green space zones, at 16.40% and
14.71%, respectively. POP was the most important factor for
industrial, residential, and transportation zones, reaching
14.17%, 25.37%, and 19.80%, respectively. At night, the
contribution of the BH increased. Compared with at 11:58, the
contribution value to the public service UFZ increased from
6.86% to 23.42%, becoming the most important 3D
morphological factor affecting the UFZ. In addition, SVF was
an important factor affecting commercial UFZ, contributing
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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10
9.45%. The impact value of the FAR on the industrial UFZ
increased by 4.59% compared to 11:58.
D. Marginal effects of 2D/3D urban morphological drivers on
diurnal LSTs
To compare the impact of the relevant parameters of
different UFZs on LST, two extreme values were selected at
01:32 and 11:58. Considering that the other two time points,
09:37 and 19:51, had certain similarities with these, four
significant indicators were selected according to their
contribution to LST, considering their marginal effects on LST.
At 01:32, four indicators, NDVI, DEM, BH, and POP, were
selected, with a total contribution rate of 45.69–56.97%. At
11:58, NDBI, NDVI, PCL, and DEM, were selected, with a
total contribution rate of 47.77–60.33%. The horizontal and
vertical axes in Fig. 8 and Fig. 9 reflect the extent of variation
of each driver and its effect on LST. The vertical
coordinates >0, 0, and <0 indicate positive, no, and negative
correlations with LST, separately. The larger the absolute
value, the greater the impact value.
At 11:58, LST reached its peak value of the four research
moments. The NDBI showed a crucial warming effect in
various UFZs, and PCL also played a crucial role in affecting
LST. The proportion of constructed land of 40–50% indicated
the shift, beginning with a negative correlation and concluding
with a positive correlation. A crucial warming effect was
observed as the PCL values increased. In urban planning, it is
advisable to avoid blind expansion to mitigate the heat-island
effect. The NDVI represents vegetation coverage; greater
values signify abundant greenery and shaded regions. When
the NDVI value was >40%, it had a strong cooling effect,
particularly in industrial areas where the threshold was as low
as 10%.
At 01:32, when the NDVI was approximately 0.4, it
exhibited a negative correlation with LST, significantly
cooling public activity areas such as businesses, public
services, and transportation. When the DEM was 60–90 m, a
positive link with LST was seen due to the increased human
activity in those areas. Beyond this boundary, the two values
exhibited a negative correlation. Owing to the need for terrain
flatness in industrial areas, this value was advanced to 28 m.
The BH critical point of UFZs with public activities, such as
commerce, public services, residences, and green spaces, was
50–60 m, while that of industrial and transportation zones was
10–20 m. When this value was exceeded, a warming effect
occurred. Therefore, in urban planning administration,
reducing the construction of super high-rise buildings is crucial
for enhancing perceived comfort.
V. DISCUSSION
A. Spatiotemporal heterogeneity of diurnal LST variations
Spatiotemporal variations in daytime and nighttime LSTs
exhibited a distinct inverted U-shaped trend. At night, high-
temperature zones were mainly located in water bodies,
whereas low-temperature zones were primarily found in areas
with dense vegetation, such as the eastern mountains and hills.
During the daytime, areas of vegetation also showed lower
temperatures. Ibsen et.al [72] explored that in semi-arid areas,
the increased areas of vegetation, especially tree canopy, plays
an important role in reducing solar radiation and lowering
surface temperature. The absence of solar radiation at night
also has the same effect, which is consistent with our results.
Water bodies gradually transitioned from being cold sources to
hot spots. At the same time, the LST of impermeable surfaces
in built-up areas increased rapidly, with the LST values rising
from low to high. The highest LST in the four time periods
studied occurred at 11:58, further strengthening this trend. This
phenomenon occurred because materials such as building
metals, which have low specific heat capacities, heat up
quickly in sunlight and cool down rapidly after sunset,
resulting in very high LSTs during the day, especially at noon,
and lower LSTs at night [73], [74]. Water has a high specific
heat capacity, so it warms much slower when absorbing equal
amounts of heat energy; thus, it becomes a heat island area in
the city during the day. The energy absorbed during the day is
gradually released at night, resulting in elevated LST values
[75]. The cooling effect of water bodies has been further
enhanced in riverside and coastal cities [76].
The LST variation between daytime and nighttime across
various UFZs is shown in Fig. 4. During the day, the
temperature in industrial areas was the highest. In this respect,
the operation of industrial equipment generates a large amount
of heat, causing the temperature to increase. Higher
temperatures are typically found in residential, commercial,
and public service areas [77], [78], [79]. and this is primarily
because UFZs consist of impermeable surfaces that heat up
more quickly than natural surfaces, and they are also
influenced by human activities. Chen et al. [80] found that
public service facilities contribute the most to the thermal
environment. Since the author used data from 2020, we
speculate that due to factors such as the influence of health care,
the artificial heat generated by public service facilities such as
health care increased during this period. At present, the vitality
of industrial and commercial facilities has recovered, so the
impact of industrial facilities on the thermal environment has
increased. We also found that the high-temperature
phenomenon in the green spaces and squares was weak. This
relates to the large-scale vegetation coverage enhancing the
thermodynamic characteristics of the underlying surface,
facilitating air cooling and circulation, and thereby exerting a
substantial regulatory influence on LST, as determined in other
studies.
B. Variations in the impact of urban morphology on diurnal
LSTs across different UFZs
Some studies have drawn different conclusions when
studying urban morphological factors using ECOSTRESS
LST data. Lin et al. [81] believed that the anthropogenic heat
in Fuzhou impacted LST at night. In contrast, in the present
study, we found that the relevant construction indicators had a
greater impact during the daytime. Fuzhou is an inland city
located near the eastern coast, whereas Shenyang is a city at a
higher altitude located further inland. Therefore, the climate
difference between the two locations is significant, especially
when the humidity conditions differ. Shenyang is an industrial
city with additional industrial heat production. Therefore, the
daytime was affected more by anthropogenic heat.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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11
Fig. 9. Marginal effect of urban morphology drivers on land surface temperature at 11:58.
Extensive studies have investigated how the USF influences
the thermal environment, as well as the effects of 2D/3D
morphology on LST [82]. Generally, 2D drivers have a greater
impact on daily LST variations than 3D drivers. In this study,
the impact of the 2D drivers on daily LST variations tended to
that of 3D drivers. In comparison to previous studies, we
determined that the DEM was the predominant factor affecting
daytime LST; as altitude increases, variations in slope
orientation and gradient impact how the surface absorbs solar
heat energy, bringing about differences in LST, which
consistent with previous reports [83]. In addition, the NDBI and
PCL contributed significantly to the LST of the commercial,
industrial, public service, and other zones. With the increase in
human activities, the importance of indicators related to urban
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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12
construction increased. Surface biophysical parameters like
NDVI contribute significantly to public service areas.
Vegetation can evaporate water to keep the temperature low.
These areas typically have extensive impermeable surfaces,
which capture and retain solar radiation while limiting
evapotranspiration, leading to higher LST levels. LST of public
service facilities is more responsive to impermeable surfaces.
Given the unique construction environment of commercial
districts, numerous factors influence LST.
Chang et.al [84] discovered that population was correlated
more with nighttime surface temperature than daytime, and this
is consistent with the results of our related study. In addition,
POP was found to be an important factor affecting residential
and industrial areas at night, which previous studies have not
mentioned [85]. It is speculated that residents mostly rest at
home at night, although people may work night-shifts in
factories. However, commercial, public service and other areas
are mostly closed at night; therefore, the impact is less. In
addition, 3D factors, such as BH and BD, are important
influencers of daytime LST in functional areas such as
residential and public services, because taller buildings can cast
more shadows and affect LST simultaneously. However, Chen
et al. [86] found that BH had a weakening effect on daytime
LST in winter, which may have been due to a decrease in both
the duration and intensity of sunlight, resulting in a significantly
lower LST. Hence, the importance of 3D morphological
parameters is crucial when studying the thermal conditions of
densely populated urban areas. Urban planning authorities need
to strategically regulate building control indicators to mitigate
heat absorption and alleviate UHIs.
C. Marginal effects of urban morphology on diurnal LSTs in
different UFZs
Although studies have analyzed the marginal effect of urban
morphology and surface temperature, few have investigated the
difference between day and night from the perspective of
different functional areas. In this study, we made some new
discoveries. Suthar et al. [87] found that the irregular increase
in built-up areas was an important reason for the increase in
surface temperature in summer. In this study, PCL and NDBI
were two important warming factors during the day, and this is
consistent with the results of previous studies. Significant
positive correlations with LST reached 0.1 and 0.45,
respectively. Therefore, the increase in construction on land
should be strictly controlled in highly urbanized areas.
Furthermore, the NDVI was an important cooling indicator
affecting daytime and nighttime LST. Unlike observations in
other studies, we found that the NDVI had a greater effect on
industrial and public service areas, and the cooling effect was
the most obvious when the NDVI was >0.1. Solar radiation can
be blocked by expanding green areas and increasing vegetation
coverage, leading to a better cooling effect. Chen et al. [88]
found that the DEM was negatively correlated with LST.
However, the present study found that the DEM had a complex
effect on the daytime LST of zones such as residential and
commercial services. When the DEM value was <20 m or >80
m, the transpiration between vegetation was strong, promoting
air cooling. The LST in the middle area increased because of
the concentration of human activities.
During daytime, when the BH increased, the heat exchange
increased, and the LST was reduced to a certain extent; this
aligns with the findings of other studies and is related to shaded
areas reducing solar radiation [89]. However, unlike other
studies, we found that the intensity of sunlight at night was more
limited than during the day and solar radiation decreased
accordingly; therefore, the opposite conclusion was drawn.
When the BH was >40 m, it had a certain wind-shielding and
heat-insulating effect and a positive correlation with the LST.
However, the critical threshold differed [90], [91]. Mo et al. [92]
found that when the building height was greater than 60 m, the
cooling effect of residential areas was more significant. Some
researchers believe there may be a positive relationship
between climate, horizontal distance, and the solar elevation
angle between buildings [85], but relevant research is required.
D. Impact on urban planning and management
The scientific planning of urban spatial layouts profoundly
impacts the thermal environment. The gradient of urban
expansion can be divided into the following three levels in this
study: the first level is the urban center, including the southern
part of Huanggu District, the eastern part of Yuhong District,
the northern part of Heping District, the western part of Shenhe
District, and the northern part of Hunnan District. These areas
are mostly public activity areas, with more heat release and
limited air circulation and heat mitigation capabilities.
Considering the development density of the site and the land
value, it is unlikely that major adjustments in the spatial
structure will be made. However, a landscape regulation system
for heat mitigation can be established by establishing a greening
cooling system and increasing vegetation coverage. The second
level is the northern part of Huanggu District, the southwestern
part of Yuhong District, the southwestern part of Dadong
District, the eastern part of Shenhe District, the western part of
Hunnan District, and the northeastern part of Tiexi District.
These areas have a medium-level building density distribution
and artificial heat release, with certain industrial activities and
a relatively low greening rate. In these areas, the urban spatial
structure can be appropriately adjusted and improved, a
favorable air circulation environment can be created, and
greening can be encouraged. In particular, the industrial area in
Tiexi District can cool the environment by increasing the
greening area and optimizing the industrial layout. The third
level is the southern part of Shenbei New Area, the eastern part
of Hunnan District, the northeastern part of Dadong District, the
eastern part of Shenhe District, and the northwestern part of
Sujiatun District. From the perspective of location, these areas
are located at the edge of the built-up area where there is a low
construction density. We found that NDBI and PCL were
important influencing factors during the daytime. According to
the relevant requirements of national land space planning, it is
necessary to control the growth boundary of urban areas and
improve resource utilization and structural adjustment through
effective space. BH and BD were found to be the main three-
dimensional factors affecting the daytime and nighttime surface
temperature. Therefore, maintaining a reasonable building
height difference and building density during the planning and
construction of the new area can help to control the airflow
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
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13
between buildings, which would have a cooling effect on the
area. It is important to consider spatial patterns to enable high-
quality and sustainable advancements to be made.
E. Limitations
We explored how urban morphology affects diurnal LST.
However, our work has certain limitations. Due to the
particularity of the orbit of the International Space Station, the
difficulty in obtaining nighttime LST data, and the influence of
cloud cover, the number of available ECOSTRESS remote
sensing images was highly limited. It was impossible to
guarantee that the day and night surface temperatures belonged
to the same day. Therefore, our data accuracy has produced
certain errors. In future research, all-weather remote sensing
data should be considered, or ground monitoring and numerical
simulation technology should be combined to obtain surface
temperature data with high temporal resolution. To enhance the
comprehensiveness of this study, we could consider the
heterogeneity of LST and urban morphology of different
landforms and developmental backgrounds and extend this
research to encompass other cities, thereby investigating both
the general applicability and distinctive characteristics of the
findings.
VI. CONCLUSION
We investigated the variability in land surface temperature
across various UFZs. We used an enhanced regression tree
model and ECOSTRESS summer daily variation data to
examine how urban 2D and 3D morphology influences daily
LST. The following are the principal conclusions that can be
drawn from this study:
(1) The UFZs of Shenyang are mainly residential and public
service areas, among them, public service land accounts for the
largest proportion, reaching 22.11%. And there was a clear
spatial heterogeneity in the daytime and nighttime LST of the
different UFZs in Shenyang's primary urban zones. At daytime,
the temperature in the industrial area was the highest, reaching
43.1°C at 11:58. The residential, commercial, and service areas
were concentrated in high-temperature areas as well. Green
squares indicate a significant cooling effect. At night, the water
becomes a heat source in the city.
(2) Normalized difference vegetation index and DEM were
important factors affecting various UFZs. Construction-related
indicators, such as PCL and NDBI significantly contributed to
daytime LST, and their combined contribution to commercial
and residential functional areas reaches about 30%. Nighttime
POP is an important factor affecting LST in residential and
industrial areas, with contribution rates of 22.06% and 16.77%
at 01:32 respectively. In addition, 3D factors such as B H and
BD, played a synergistic role with 2D factors on LST.
(3) With respect to the marginal effect, when PCL >45%,
there was a substantial warming effect on the daytime and
nighttime LST. Owing to the appearance of building shadows,
the effect of BH on LST showed variation during daytime and
nighttime. There was noticeable cooling observed when the BH
exceeded 40 m during the day. However, at night, as the BH
increased, it had a warming impact on the LST after the critical
point. NDVI has a significant cooling effect on each UFZs,
especially the industrial functional area, where the threshold is
10%, and LST can be reduced by expanding green areas.
Urban planners can enhance the thermal environment
through well-designed spatial layouts and strategic plans,
fostering resilient urban growth. This study also provides
valuable insights for guiding future decision-making, planning,
and strategies aimed at mitigating the UHI effect.
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Qianmin Zhang received the bachelor’s
degree in urban planning in 2023 from
Jiangho Architecture, Northeastern
University, Shenyang, China, where she is
currently working toward the master’s
degree in urban and rural planning with the
Jiangho Architecture.
Her research interests include urban
planning, urban thermal environment and
urban function zone.
Jun Yang received the Ph.D. degree from
Liaoning Normal University, China, in
2009. He is a Professor in the effect of
human settlement and GIS with Liaoning
Normal University, Dalian, China, and a
professor of Jiangho Architecture,
Northeastern University, Shenyang, China.
He has been committed to urban space
growth, urban thermal environmental,
cellular automata land use change, and urban human
settlements, as well as other aspects of research.
Xinyue Ma graduated from Jiangho
Architecture, Northeastern University,
Shenyang, China, in 2021, with a bachelor's
degree. She is currently working toward the
master's degree with the Jiangho
Architecture, Northeastern University.
Her research interests include urban
planning, urban climate and carbon
emissions.
Jiaxing Xin is currently working toward the
Ph.D. degree in land resources management
with Northeastern University, Shenyang,
China.
Her research interests include urban
thermal environment, land use policy and
local climate zone.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
18
Jiayi Ren is currently working toward the
Ph.D. degree in land resources management
with Northeastern University, Shenyang,
China.
She has been committed to assessment of
urban thermal environment and land use
policy.
Wenbo Yu is currently working toward the
Ph.D. degree in land resources management
with Northeastern University, Shenyang,
China.
His current research interests include
spacetime evolution of human settlements,
urban thermal environment, and urban
landscape analysis.
Xiangming Xiao received the Ph.D. degree
in ecology from Colorado State University,
Fort Collins, CO, USA, in 1994.
He is currently a Professor with the
School of Biological Sciences, University of
Oklahoma, Norman, OK, USA, and the
Director of the Earth Observation and
Modeling Office. He has been committed to
land use and land cover change, the carbon
cycle, and ecological environment analysis of infectious
diseases.
Jianhong Xia received the Ph.D. degree in
geo-graphic information science from
Liaoning Normal University, Dalian, China,
in 1996.
She is a Professor with the School of Earth
and Planetary Sciences, Curtin University,
Perth, Australia. She has worked as a
Transport Geographer and Transit Planner
with a range of research experience in
relation to tourism, public transport development, driving,
spatial navigation and way finding, and human mobility.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2024.3455791
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
