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Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

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In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.
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Citation: Chongtaku, T.;
Taparugssanagorn, A.; Miyazaki, H.;
Tsusaka, W.T. Integrating Remote
Sensing and Ground-Based Data for
Enhanced Spatial–Temporal Analysis
of Heatwaves: A Machine Learning
Approach. Appl. Sci. 2024,14, 3969.
https://doi.org/10.3390/
app14103969
Academic Editors: Samuel Adelabu,
Romano Lottering and Kabir
Peerbhay
Received: 24 March 2024
Revised: 2 May 2024
Accepted: 5 May 2024
Published: 7 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Integrating Remote Sensing and Ground-Based Data for
Enhanced Spatial–Temporal Analysis of Heatwaves:
A Machine Learning Approach
Thitimar Chongtaku 1, Attaphongse Taparugssanagorn 2,* , Hiroyuki Miyazaki 3and Takuji W. Tsusaka 4
1Remote Sensing and Geographic Information Systems, Department of Information and Communications
Technologies, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4,
Klong Luang 12120, Pathum Thani, Thailand; st119790@ait.asia
2
Telecommunications, Department of Information and Communications Technologies, School of Engineering
and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang 12120, Pathum Thani, Thailand
3Center for Spatial Information Science, University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa-shi 277-8568, Chiba, Japan; heromiya@csis.u-tokyo.ac.jp
4Natural Resources Management, Department of Development and Sustainability, School of Environment,
Resources and Development, Asian Institute of Technology, P.O. Box 4,
Klong Luang 12120, Pathum Thani, Thailand
*Correspondence: attaphongset@ait.asia
Abstract: In response to the urgent global threat posed by human-induced extreme climate hazards,
heatwaves are still systematically under-reported and under-researched in Thailand. This region
is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths,
underscoring a pressing issue that needs to be addressed. This research article is one of the first to
present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and
geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN),
heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave
amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-
based air temperature (
Tair
) observations from 1981 to 2019. The result reveals significant marked
increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most
pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a
0.27-day/year
in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experi-
encing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN,
1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (
p<
0.05) reveals spatial
heterogeneity in heatwave dynamics, with robust daytime correlations between
Tair
and LST in
rural (HWN, HWF, HWD,
r>
0.90) and peri-urban (HWM, HWA,
r>
0.65) regions. This study
emphasizes the importance of considering microclimatic variations in heatwave analysis, offering
insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with
ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This
approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model
for managing similar microclimates, extending the applicability of this study. Overall, the study
provides policymakers and stakeholders with potent tools for climate action and effective heatwave
management. Furthermore, this research contributes to mitigating the impacts of extreme climate
events, promoting resilience, and fostering environmental sustainability.
Keywords: heatwaves; data gap-filling; remote sensing; satellite data; air temperature; machine
learning; random forest; geospatial artificial intelligence; natural hazard; Thailand
1. Introduction
Heatwaves, acknowledged as silent killers, are defined as prolonged periods of exces-
sively hot weather, which may also be accompanied by high humidity and are considered
Appl. Sci. 2024,14, 3969. https://doi.org/10.3390/app14103969 https://www.mdpi.com/journal/applsci
Appl. Sci. 2024,14, 3969 2 of 27
among the deadliest climatic hazards [
1
]. Over the initial two decades of the 21st century,
a multitude of severe heatwaves had a profound impact on human health, agriculture,
water resources, energy demand, regional economies, and forest ecosystems [
2
]. Extreme
events of this phenomenon have far-reaching and detrimental consequences at global,
national, and local levels. They impose significant impacts on societies, including increased
morbidity and mortality rates, overwhelming healthcare systems, diminished agricultural
and ecosystem productivity, and substantial economic losses [
3
12
]. Furthermore, heat-
waves exacerbate the vulnerability of built environments, leading to increased energy
demand for cooling and a heightened risk of infrastructure failure. Climate change, rec-
ognized as a major influencer of global damage, is exacerbating heatwaves, making them
hotter, longer-lasting, and more frequent, thereby amplifying their impacts on people,
property, communities, and the environment [13].
In particular, urban environments are significantly more susceptible to elevated tem-
peratures due to the ’urban heat island (UHI)’ effect [
14
,
15
]. This susceptibility is further
exacerbated by multiple factors, such as limited green spaces, high population density,
compromised air quality, restricted availability of cooling resources, and a high concen-
tration of buildings [
16
]. However, vulnerability to heatwave effects extends beyond the
city center, indicating that suburban residents are also at risk. These areas, characterized
by their heightened sensitivity and limited adaptive capacities, experience particularly
high risks of heat-wave-related mortality [
17
]. Therefore, the observed pattern of extreme
heat phenomena not only underscores a critical gap in our current understanding but
also highlights the urgent necessity for in-depth studies with a precise focus on diverse
environments to ensure a comprehensive assessment of effects across varied areas.
Heatwaves are a serious threat in many countries throughout the world. Between 1998
and 2013, extreme heat events were responsible for over 100,000 deaths across 164 locations
in 36 countries, a number that rose to 166,000 by 2017 [
18
]. The unprecedented heatwave of
2019 caused roughly 5000 deaths in Europe, Asia, and Australia amid record-breaking tem-
peratures. The summer of 2022 saw another series of record highs, impacting South Asia,
North America, Europe, and China, and resulting in an estimated 15,000 heat-related fatali-
ties in Europe alone [
19
,
20
]. Moreover, projections from the Eurostat mortality database
indicate a troubling rise in heatwave-related deaths globally, with estimates predicting
increases to 68,000 by 2030, 94,000 by 2040, and 120,000 by 2050 [
21
]. Predictive models
forecast a surge in the frequency of these extreme temperature events, expected to be up
to seven times more frequent in the next three decades [
22
,
23
]. Alarmingly, heatwaves
have intensified rapidly in some Asian regions, particularly in South and Southeast Asia,
where the number of heatwave days has increased by 4.2 days per decade, compared to
the global average of 2.26 days per decade [
24
27
]. Thus, the increasing occurrence of
high-magnitude and impact heatwaves has raised concerns worldwide and has attracted
increasing interest in this issue among researchers over the past decade [
28
]. For that reason,
these observations expose a critical gap in the need for thorough research on heatwave
phenomena, especially in the context of ongoing climate change.
Southeast Asia, highly susceptible to the detrimental impacts of extreme heat, faces
significant challenges [
29
]. The increase in heatwave amplitude has shown a linear growth
in relation to global warming levels, with distinct regional differences between the Mar-
itime Continent and the Indochina Peninsula due to their differing heat content in lower
atmospheric boundaries. The trend towards more frequent, prolonged, and intense heat-
waves is projected to continue, exacerbating public health challenges and underscoring the
imperative for comprehensive adaptive strategies to mitigate the impacts of these devas-
tating climate phenomena, particularly in countries like Thailand [
30
]. Thailand itself has
experienced fluctuations in climate, particularly in rainfall patterns and temperature [
31
].
With its relatively underdeveloped public health infrastructure and highly vulnerable pop-
ulations [
32
], the region is confronting increasingly severe climate-related issues. Notable
increases in temperatures in recent years have led to alarming health repercussions, in-
cluding a significant rise in heat-related deaths [
33
]. Between 2015 and 2018, heatwaves in
Appl. Sci. 2024,14, 3969 3 of 27
Thailand led to 158 deaths, primarily in the northern and central provinces, where extreme
temperatures are directly linked to higher mortality rates [
34
]. Concurrently, there has been
a rise in respiratory and cardiovascular diseases, now among the top five causes of death
and disability in the country [
35
]. Despite these challenges, current heatwave management
practices remain underdeveloped. Ref. [
36
] recommends an integrated approach to urban
planning and design to mitigate heat stress. Critical measures include the development
of green open spaces to reduce urban heat island effects and the implementation of green
building codes, which advocate for features like rooftop gardens and heat-reducing materi-
als. Furthermore, establishing heat early warning systems and collaborative emergency
response plans is vital for enhancing urban resilience against heat stress, particularly in
cities like Bangkok.
Satellite remote sensing offers significant promise for the precise study and detection of
heatwaves from a broad perspective, aiding in the comprehension of heatwave patterns [
37
].
Defined by its high spatial resolution and temporal frequency, this approach allows for
extensive analyses, yielding regular and long-term data crucial for grasping the behavior
and effects of heatwaves. This technique overcomes the limitations posed by unavailable
or poorly distributed ground station networks [
38
,
39
]. Previous studies [
16
,
40
46
] have
demonstrated the capability of multitemporal remote sensing data from several satellites to
analyze, map, and monitor the spatial and temporal dynamics of heatwaves.
Furthermore, studies by [
47
,
48
] have shown that thermal satellite-derived LST is
a crucial parameter for identifying heatwaves and understanding the consequences of
extreme heat. Additionally, LST plays a vital role as a key input in the study of land surface
water and energy budgets at both local and global scales [
49
]. LST, recognized as one of the
essential climate variables (ECVs) by the World Meteorological Organization (WMO), is a
key indicator for both climate change and land surface processes. This is due to the heat
exchange between the land surface and the near-surface atmosphere, making the dynamics
in air temperature and LST consistent [
50
,
51
]. Various satellites with Thermal Infrared (TIR)
airborne sensors, including the Advanced Very-High-Resolution Radiometer (AVHRR),
the Moderate Resolution Imaging Spectroradiometer (MODIS), and LANDSAT, have been
used for this purpose [
39
,
52
54
]. Among these, data from MODIS have been pivotal for the
retrieval of LST dynamics and trends, providing the longest consistent time series covering
vast global regions [
55
]. Its global radiometric resolution and dynamic ranges, along with
accurate calibration in multiple TIR bands, are well designed [
56
]. Furthermore, MODIS
offers the advantage of revisiting the same area four times daily, ensuring detailed and
timely data collection. However, the process of acquiring LST data from TIR observations
often encounters obstacles due to the presence of cloud cover, which introduces widespread
data gaps, affecting statistically more than 60% of the global extent [
57
]. This issue poses
substantial challenges in analyzing the spatial and temporal variations of LST.
1.1. Research Gaps
To date, in response to these challenges, an extensive array of research initiatives
have been undertaken with the aim of developing sophisticated techniques for the recon-
struction of missing data within LST datasets [
49
,
58
62
]. A number of methods have been
developed, which can be generally divided into three types according to the sources of
reference information: (1) spatial information, (2) multitemporal observations, and (3) other
complementary data, for example, from ground meteorological stations [
59
]. However,
techniques that rely on comparing cloudy-sky pixels to nearby clear-sky ones are effective
only in images with minimal cloudiness [
62
,
63
]. Although meteorological station data can
provide the temperature of the point where the station is located, accurate and precise heat
wave analysis requires continuous temperature distribution [
41
,
64
]. To address these gaps,
an improved method for reconstructing LST in cloud-covered areas has been proposed.
This method employs a linking model that integrates MODIS-LST with other surface vari-
ables, such as surface topography and land cover conditions, through a machine learning
algorithm known as random forest (RF) regression [
49
]. Owing to its robust predictive
Appl. Sci. 2024,14, 3969 4 of 27
performance and capability to process complex, non-linear data, this approach not only
enables accurate predictions of various extreme climate phenomena but also facilitates a
comprehensive evaluation of the significance of different temporal and spatial features in
predicting LST.
1.2. Our Contributions
Heatwaves denote prolonged periods of exceptionally hot weather, often coupled with
high humidity, exerting severe repercussions on human health and the environment [
1
].
Despite extensive research endeavors [
3
12
], including investigations into urban areas
vulnerable to the ‘urban heat island (UHI)’ effect [
14
,
15
], our current grasp of heatwave
effects remains incomplete. This gap underscores the need for comprehensive studies
focusing on diverse environments to ensure a thorough assessment of heatwave impacts
across varied areas.
Our contribution addresses this critical gap by introducing a novel approach for un-
derstanding heatwave dynamics, particularly in data-deficient regions vulnerable to severe
heatwave events in Thailand. We aim to elucidate the complex temperature dynamics
in such regions, encompassing urban (Bangkok), suburban (Pathum Thani), and rural
(Saraburi) locations. Leveraging LST as a vital indicator for heatwaves, we propose an inno-
vative, integrated RF model that combines satellite-derived LST data with air temperatures
and spatial and temporal features. Unlike previous methods [
16
,
37
,
40
46
] reliant solely on
remote sensing data or ground-based observations, our model bridges the gap between the
two sources, enhancing the accuracy and reliability of heatwave predictions.
Satellite remote sensing holds promise for studying heatwaves due to its high spatial
resolution and temporal frequency [
47
,
48
]. However, cloud cover often obstructs LST
data acquisition, leading to widespread data gaps [
57
]. Our proposed method overcomes
this challenge by integrating MODIS-LST with other surface variables, such as surface
topography and land cover conditions, through RF regression. This integration enhances
the predictive performance of our model, enabling accurate predictions of various ex-
treme climate phenomena and facilitating a comprehensive evaluation of spatial–temporal
heatwave patterns across diverse environments. Thus, our study represents a significant
advancement in understanding heatwave dynamics and provides valuable insights for
effective heatwave risk management and climate resilience strategies.
Overall, our findings underscore the significant role of earth observation and machine
learning in shaping sustainable climate strategies. Notably, recent advancements in ma-
chine and deep learning approaches have markedly enhanced prediction accuracy and
research outcomes over traditional methods. These developments are pivotal in inform-
ing governmental policies and guiding decision-making processes, leading to improved
resilience in both urban and non-urban environments against climate extremes and driving
efforts toward a more sustainable future.
2. Materials and Methods
This section describes the geographical context, data collection methods, and analytical
techniques, providing insights into the scientific framework that underpins the investiga-
tion of heatwave dynamics in Thailand’s central region. It begins with a description of the
study area, followed by an exploration of the data sources used in the analysis. The section
then outlines the methodologies employed for the spatial–temporal quantification of heat-
waves and the spatial correlation analysis, highlighting the novel approaches adopted to
achieve the research objectives.
2.1. Study Area Description
In this study, we focus on urban, peri-urban, and rural regions within the central region
of Thailand as depicted in Figure 1. These classifications adhere to national standards
and are based on criteria such as administrative divisions, demographic profiles, primary
land use, and prevalent occupations as cited in [
65
,
66
]. Therefore, the selected study areas
Appl. Sci. 2024,14, 3969 5 of 27
are systematically categorized into urban, peri-urban, and rural types according to these
variables. Additionally, these areas are situated within a tropical monsoon climate zone,
characterized by three distinct seasons: summer (mid-February to mid-April), rainy (mid-
April to mid-October), and winter (mid-October to mid-February). The selected regions are
as follows:
Figure 1. Location of the study area in Thailand and spatial distribution of the meteorological stations
in dotted print (a), altitude (b), and land use (c).
Urban—Bangkok province is located at latitude 13
38
N and longitude 100
54
E.
This area is flat and low-lying, with altitudes ranging from 1.50 m to 2.0 m. It occupies
1600
km2
and consists of 50 districts with a registered population of 5.52 million,
making it the most highly inhabited city in Thailand. The built-up area covers 67.36%,
while agricultural land constitutes only 0.16% of the total area. The annual mean air
temperature ranges from 28.0
C
to 30
C
, with the average maximum air temperature
in April ranging from 33.0
C
to 38.0
C
, and the average minimum air temperature
in January ranging from 16.0 C to 25.0 C.
Peri-urban—Pathum Thani province is situated at latitude 14
01
N and longitude
100
32
E, with an average altitude of 2.30 m. It covers an area of 1519
km2
and
comprises 7 districts with a registered population of 1.19 million. The annual mean air
temperature ranges from 28
C
to 30
C
, with the average maximum air temperature
in April ranging from 32
C
to 34
C
, and the average minimum air temperature in
December ranging from 24.0
C
to 26.0
C
. This area is characterized by 29.79% of
the land serving as city outskirts, accommodating dense dwellings, industrial estates,
and being devoid of forest areas.
Rural—Saraburi province is situated at latitude 14
31
N and longitude 100
54
E,
with the landscape ranging from 2.0 m to 10.0 m in elevation, covering an area of
3576
km2
. This region includes 13 districts and has a population of 643,963 registered
Appl. Sci. 2024,14, 3969 6 of 27
residents. The average annual air temperature is 28.2
C
, with highs of 31.4
C
in
April and lows of 23.6
C
in January. Approximately 78% of the land consists of
agricultural and forested areas.
2.2. Data Sources
2.2.1. Ground-Observed Air Temperature
Ground-based air temperature data, including daily maximum air temperature (
Tmax
)
and daily minimum air temperature (
Tmin
), from 1981 to 2019, were acquired from the
meteorological stations operated by the Thai meteorological department (TMD). These
observations are measured at 2.0 m above the ground according to the world meteorological
organization (WMO) standard [
67
]. In this research, temperature data from 10 meteorologi-
cal stations are utilized as depicted by the dotted points in Figure 1a. These stations are
classified into urban, peri-urban, and rural categories based on their geographical locations
as outlined in Table 1. As there is no ground station observed in Sara Buri province, which
represents the rural area, this study designates the meteorological stations in Ayutthaya,
Lopburi, and Nakhon Ratchasima to serve as proxies for this inactive area.
Table 1. Meteorological station details and available data time period.
Station ID Station Name Province Altitude (m) Type of Area Time Period (years)
1 UBKP Bangkok Port (Khlong Toei) Bangkok 1 Urban 1994–2019 (26)
2 UBKK Bangkok (Queen
Sirikit National
Convention Center) Bangkok 4 Urban 1981–2019 (39)
3 UTMD Thai Meteorological
Department (Bang Na) Bangkok 3 Urban 1981–2019 (39)
4 UBKD Don Muang Airport Bangkok 5 Urban 1981–2019 (39)
5 PSVN Suvarnabhumi Airport Samut Prakan 2 Peri-urban 2008–2019 (12)
6 PPTN Pathum Thani
Agrometeorological Station Pathum Thani 9 Peri-urban 1998–2019 (21)
7 RAYT Ayutthaya Meteorological
Station Ayutthaya 12 Rural 1993–2019 (27)
8 RLBR Lopburi Meteorological
Station Lopburi 20 Rural 1981–2019 (39)
9 RBCL Bua Chum Meteorological
Station Lopburi 54 Rural 1981–2019 (39)
10 RPCN Pak Chong Meteorological
Station Nakhon Ratchasima 422 Rural 1981–2019 (39)
2.2.2. Remotely Sensed Land Surface Temperature
The MODIS sensors, aboard NASA’s Terra and Aqua spacecraft, launched in 1999
and 2002 respectively, play a pivotal role in global studies of Earth’s surface, atmosphere,
cryosphere, and ocean processes [
68
]. These instruments capture data across 36 spectral
channels from 3 to 15
µ
m, ranging from visible to infrared wavelengths. Data are quantized
to 12 bits and offer a spatial resolution of approximately 1 km at nadir, with overpass
times at approximately 10:30 and 22:30 local solar time (Terra) and 13:30 and 01:30 (Aqua)
of any location on Earth every 1–2 days [
69
]. Specifically, LST data are generated from
thermal infrared bands 31 and 32 (at 11 and 12
µ
m) using a generalized split-window (GSW)
algorithm with an accuracy around 2.0 K [
70
]. This physics-based algorithm, developed to
address challenges such as atmospheric transmission, path radiance, downward thermal
irradiance, and solar diffuse irradiance, simultaneously retrieves surface band-averaged
Appl. Sci. 2024,14, 3969 7 of 27
emissivities and temperatures. It efficiently processes temperature measurements from
day/night pairs of MODIS data [38,56,71].
Two specific MODIS-LST products (version 6.1), which have been enhanced through
various calibration changes, were incorporated into our analysis. Both products are sourced
from tile h27v07: the Terra daily LST (MOD11A1), spanning from 1 January 2000 to 31
December 2019, and the Aqua daily LST (MYD11A1), covering the period from 1 January
2002 to 31 December 2019. We selected these time frames to provide a comprehensive view
of temperature anomalies over nearly two decades. For data extraction and analysis, we
utilized the A
ρρ
EEARS application, which is accessible at https://appeears.earthdatacloud.
nasa.gov/. This tool facilitates efficient access to geospatial data, enabling the point
extraction of MODIS-LST and supplementary data.
2.2.3. Elevation and Land Use/Land Cover Data
The study area’s elevation was determined using the publicly accessible shuttle radar
topographic mission (SRTM) digital elevation model (DEM) from the USGS earth explorer
platform. The DEM has a spatial resolution of 90 m. Subsequently, the data at this res-
olution were averaged to achieve a finer 10 m resolution as demonstrated in Figure 1b.
Additionally, land use/land cover data from the land development department (LDD) of
Thailand were incorporated. These raster-format data cover specific regions, including
Bangkok, Pathum Thani, Samut Prakan, Ayutthaya, Lopburi, and Nakhon Ratchasima,
spanning the years 2000–2019. The data are classified according to the Level 1 LDD stan-
dard classification criteria, comprising five classes as illustrated in Figure 1c: built-up area
(U), agricultural land (A), forest area (F), water body (W), and miscellaneous land use (M).
This classification aids in comprehending regional land use patterns and their potential
impact on local temperatures.
2.3. Methods
This section outlines the methodology used to examine the spatial–temporal quantifi-
cation of heatwaves and to measure the degree of spatial heterogeneity. It aims to present
the potential of LST-based satellite data for understanding and monitoring heatwave events
in different regions of Thailand. Initially, the approach encompasses data collection from
diverse sources. Then, it addresses missing data through imputation due to technical mal-
functions. Subsequently, the methodology involves identifying heatwave metrics. Finally,
it visualizes the spatial–temporal patterns of heatwaves to align detected heatwaves
Tair
with LST, thus presenting a comprehensive processing sequence as illustrated in Figure 2.
Figure 2. Data utilization, methodology, and study findings in relation to the organization of sections
within this research paper.
Appl. Sci. 2024,14, 3969 8 of 27
2.3.1. Data Gap-Filling
In the preprocessing phase, addressing the challenge of missing values is vital to
ensure the integrity and precision of climate-data-driven predictions. The dataset un-
dergoes optimization through the systematic removal or statistical imputation of these
missing values, rendering it more suitable for subsequent predictive modeling or cluster
analysis functions.
Spatial–Temporal Ground-Observed Air Temperature Gap-Filling
In the event of malfunctioning at the meteorological station, the
Tair
dataset encounters
partial incompleteness. Recognizing the missing data adherence to a pattern of missing
at random (MAR), ref. [
72
] suggests employing straightforward imputation methods to
mitigate these gaps. When the extent of missing data remains below 5 percent, a mean
substitution approach is advocated. This entails replacing missing values with the mean
computed from observed values spanning multiple years.
However, if the percentage of missing data exceeds 5% or spans over a month, a re-
gression model is employed.
Regression models, whether utilizing a single closest neighbor station [
73
] or multiple
nearby stations [
74
76
], have proven effective in estimating daily weather observations [
74
,
77
79
]. The imputation processes, which are applied to both
Tmax
and
Tmin
, are represented
by
E(Y|X) = β0+β1X, (1)
where
Y
is the predicted missing air temperature,
X
is the reference data from a neighboring
station,
E(Y|X)
is the expected value of
Y
given the values of
X
,
β0
is the intercept, and
β1
is the slope.
Spatial–Temporal Satellite-Based Land Surface Temperature Gap-Filling
Addressing gaps in daily LST data derived from MODIS platforms poses a signifi-
cant challenge, necessitating innovative solutions for data reconstruction and prediction.
Recent studies have explored various methods, including reconstructing LST from satel-
lite datasets, forecasting daily LST using time series data, and estimating subpixel LST
by fusing multisource data [
80
83
]. Although machine learning techniques have been
introduced to LST retrieval, their application remains limited [
84
]. To bridge this gap, we
employ the RF machine learning algorithm, celebrated for its exceptional predictive ability
and adaptability to nonlinear data [
85
]. The RF methodology of constructing multiple
decision trees provides a robust framework for classification and regression tasks, making it
particularly effective for filling gaps in LST data caused by cloud cover or other disruptions.
The RF algorithm is adept at managing high-dimensional data and unraveling complex
relationships, which is crucial for accurately predicting missing LST values. It is noted that
the RF performance is enhanced when there is a strong correlation between the target and
reference variables, a principle that holds true in the analysis of intricate environmental
datasets [86].
By leveraging RF, we can address missing data without relying on standard scalar
functions. Although these functions are useful for normalization and standardization, they
can potentially introduce bias. Instead, our approach focuses on utilizing the Python-based
scikit-learn module [
87
] for efficient gap-filling, ensuring that the integrity and natural
variability of the LST dataset are maintained. This methodology not only facilitates the
prediction of missing LST values with minimal bias but also underscores the potential
of machine learning in advancing the understanding and monitoring of extreme climate
events [8891].
In RF, the prediction is made by aggregating predictions from multiple decision trees.
The prediction ˆ
yfor a given sample xis calculated by
ˆ
y=1
N
N
i=1
fi(x), (2)
Appl. Sci. 2024,14, 3969 9 of 27
where
N
is the number of decision trees in the forest, and
fi(x)
is the prediction of the
i
-th
decision tree.
Model Performance and Accuracy Assessment of Predicting Land Surface Temperature
A comprehensive set of statistical measures is computed on the test set to assess
the accuracy and performance of the predictive model for LST. These measures include
important metrics like the coefficient of determination (
R2
), root mean square error (RMSE),
minimum and maximum confidence intervals, mean absolute error (MAE), and mean bias
error (MBE) as
R2=1n
i=1(ˆ
yiy)2
n
i=1(yiy)2, (3)
RMSE ="n
i=1
(yiˆ
yi)2
n#1/2
, (4)
MAE =1
n
n
i=1
|yiˆ
yi|, (5)
MBE =1
n
n
i=1
(yiˆ
yi), (6)
where
N
is the number of records in validation datasets used in this study,
ˆ
yi
is the estimated
variable,
yi
is the observed variable,
y
is the mean of all the values, and the confidence level
is set to 0.95.
For the assessment, the calibration phase utilizes variable data spanning from 2000 to
2017, while the subsequent validation phase involves data from 2017 to 2019, following
an 80% train and 20% test split. The
R2
value, ranging from 0 to 1.0, provides a valuable
indication of the predictive model’s accuracy in estimating outcomes. The RMSE is ef-
fectively utilized to evaluate biases in both mean and spatial variance, whereas the MAE
serves as a reliable measure of error magnitude, with lower values demonstrating superior
performance. Additionally, the MBE offers insightful observations regarding the direction
of error bias, with a value of zero denoting an unbiased estimation by the model [92,93].
2.3.2. Heatwave Definition and Its Metric Detection
To date, a universally accepted definition of heatwaves remains inconsistent. Previous
investigations have adopted a variety of methodologies, leading to variations influenced
by meteorological conditions, socio-demographic attributes, acclimatization processes,
and geographic factors [
94
]. Nonetheless, a common metric employed by numerous
investigators involves the 90th percentile of daily maximum and minimum temperatures,
designated as CTX90pct and CTN90pct, respectively. This methodology has been adopted
in various studies [
95
97
], establishing a standardized and globally relevant framework
for quantifying heatwave characteristics such as frequency, duration, and intensity. Such a
standardized approach facilitates the derivation of threshold values that are instrumental
across a multitude of geographical locales and sectors of impact [
38
,
98
], proving particularly
valuable in tropical regions, including Thailand [99].
In this study, the detection and measurement of
Tair
and LST heatwaves are con-
ducted using established methods referenced in [
95
,
100
,
101
] and summarized in Table 2.
A heatwave is defined as a period when temperatures exceed a certain threshold for at
least three consecutive days, effectively identifying sustained extreme temperature events
and reflecting temporal variations. This methodology, supported by findings from [
30
],
is particularly well suited for Southeast Asia due to its complex land–sea configuration
and diverse topographies, offering a nuanced approach to detecting regional heatwave
patterns. We observe both daytime and nighttime heatwaves as widely used in [
102
106
].
Daytime heatwaves, essentially defined by high daytime maximum temperatures (
Tmax
),
are accompanied by increased downward shortwave radiation under clear skies with re-
Appl. Sci. 2024,14, 3969 10 of 27
duced cloud cover and moisture, as well as lower humidity. These hot and dry daytime
conditions can lead to potential impacts such as wildfires, water deficits, reduced crop
yields, and increased human health risks [
107
]. In contrast, nighttime heatwaves, often
measured by high nightly minimum temperatures (
Tmin
), typically occur under moist
conditions characterized by increased cloud fraction, humidity, and long-wave radiation at
the surface. These conditions significantly affect human comfort and inhibit recovery from
the heat experienced during the daytime, thus increasing the threat to human health from
high-temperature weather. More hazardous conditions emerge when extreme daytime
temperatures are combined with warm nighttime conditions for consecutive days, creating
compound heatwaves [106].
Table 2. Heatwaves indices used in the analysis.
Index Abbreviation Definition Unit Reference
Heatwave number HWN
The total number of individual heatwaves detected occurs when
temperatures exceed the 90th percentile of a given temperature
for at least three consecutive days events [95,100,101]
Heatwave frequency HWF The total number of days that contribute to heatwaves days [95,100,101]
Heatwave duration HWD The length in days of the longest heatwave days [95,100,101]
Heatwave magnitude HWM
The average of mean daily temperature throughout the duration
of heatwave C [95,100,101]
Heatwave amplitude HWA
The peak daily value in the hottest heatwave of the highest HWM
C [95,100,101]
To define daytime heatwave events, this study uses daily maximum air tempera-
tures (
Tmax
) from MODIS-MOD11A1 day and MODIS-MYD11A1 day datasets. Nighttime
events are similarly identified using daily minimum air temperatures (
Tmin
) from MODIS-
MOD11A1 night and MYD11A1 night. Heatwaves are determined using a criterion based
on the 90th percentile of the calendar day temperatures.
This threshold captures the annual variation in extreme heat, with a unique percentile
calculated for each day of the study period. As a result, this approach encompasses all
heatwave events occurring from the start to the end of the period of interest. The CTX90pct
method is utilized for
Tmax
and LST day (MOD11A1 day, MYD11A1 day), while the
CTN90pct method is employed for
Tmin
and LST night (MOD11A1 night, MYD11A1 night),
the 90th percentile threshold (T90) for a given set of temperature data Tis calculated as
T90 =Percentile(T, 90), (7)
where
Percentile(T
, 90
)
represents the temperature value below which 90% of the observa-
tions fall in the dataset (T).
2.3.3. Spatial Homogeneity in Correlation analysis
The point-to-pixel analysis method is employed to match the series of heatwave
metrics data, calculated based on
Tair
, with the corresponding pixels of gridded LST
data. Only pixels that have at least one available and used a ground-based gauge for the
calculation are included [
108
,
109
]. Given the extensive research area and the time required
for retrieving daily MODIS data, a sampling design with a buffer zone at the confluence
of the regular 5 km
×
5 km latitude and longitude grid is adopted for validating
Tair
and LST heatwaves. The use of available grids is because heatwaves may not occur at
every grid, considering the restriction of three consecutive days exceeding the threshold
temperature in the heatwave definition [
30
]. Therefore, this produces a total of 248 valid
grids encompassing three provinces of study.
To establish the spatial correlation between the pixel values of
Tair
and the correspond-
ing pixels in the detected heatwave from the LST dataset, the pixel-wise correlation is
Appl. Sci. 2024,14, 3969 11 of 27
determined using the Pearson correlation coefficient (
r
) throughout the research period.
Furthermore, the spatial–temporal relationship between a meteorological station grid and
the four nearest LST grids is determined using the median value of the four Pearson’s
r
coefficients. Notably, the
Tmax
is cross-validated with daytime LST (MOD11A1 (day) and
MYD11A1 (day)), whereas the
Tmin
is evaluated with nighttime LST (MOD11A1 (night)
and MYD11A1 (night)). Spatial homogeneity in the correlation analysis can be defined as
r=n
i=1(xi¯
x)(yi¯
y)
qn
i=1(xi¯
x)2qn
i=1(yi¯
y)2, (8)
where
r
is the correlation coefficient,
xi
is the values of the x-variable in a sample
i
,
¯
x
is the
mean of the values of the x-variable,
yi
is the values of the y-variable in a sample
i
, and
¯
y
is
the mean of the values of the y-variable.
2.3.4. Software Tools for Data Manipulation, Analysis, and Visualization
For the manipulation, analysis, and visualization of data in this study, we utilized a
comprehensive array of software tools. These included ArcMap (10.7, licensed by Asian
Institute of Technology) and QGIS Desktop (3.4.12) for spatial data analysis and mapping,
as well as R Studio and Python for statistical analysis, data processing, and graphical
representation. Microsoft Excel was also employed in conjunction with R Studio and
Python for detailed statistical analyses.
Regarding specific methodologies, the RF regression model was implemented using
the sklearn.ensemble.RandomForestRegressor from the sklearn package [
110
], ensur-
ing efficient gap-filling and preservation of the integrity and natural variability of the
LST dataset. For the MK test, we employed the Python package pyMannKendall [
111
],
which offers a robust and efficient means of calculating the test statistic, ensuring reliable
statistical results.
3. Results
3.1. Predicting Land Surface Temperature to Fill in Missing Satellite Data and Variable Importance
To assess the effectiveness of RF models in predicting LST to fill the gaps of missing
LST data, we meticulously designed an experiment incorporating both temporal and spatial
variables. Our RF models to predict LST missing data include both temporal (day of the
year (DOY) and year) indicators and spatial variables (
Tmax
,
Tmin
, elevation, built-up land,
agricultural land, forest land, water bodies, and miscellaneous land), allowing for an
evaluation of feature importance specifically for predicted LST.
To implement this approach, we systematically divided the study area into a grid
with 1 km intervals around each of the 10 meteorological stations. Subsequently, we
selected four points along cardinal directions to generate predicted LST models for both
daytime and nighttime. Following the creation of these LST prediction models, we further
divided the area into a grid with 5 km intervals, covering all seven provinces with weather
stations. This resulted in 11 training samples and 1250 testing samples. Notably, only
248 points within this grid overlapped with the targeted study area encompassing Bangkok,
Pathum Thani, and Saraburi provinces. This design ensures a rigorous evaluation of the RF
models’ predictive capabilities, accounting for both temporal and spatial factors, while also
considering the specific features of the targeted study areas.
According to the RF results, a concise overview of the model’s feature importance for
LST modeling is presented in Figure 3. The factor DOY shows the highest explanatory rates
for daytime LST as in MOD11A1_day (Figure 3a) and MYD11A1_day (Figure 3b), whereas
the
Tmin
affects most LST night as in MOD11A1_night (Figure 3c) and MYD11A1_night
(Figure 3d). In predicting LST day,
Tmax
and
Tmin
rank second, followed by year, elevation,
and land use. For MOD11A1 data, the water body from the land use factor is essential,
while the built-up area is vital for MYD11A1 data. Later, in the case of MOD11A1 and
Appl. Sci. 2024,14, 3969 12 of 27
MYD11A1,
Tmin
emerges as the second most influential factor for predicting nighttime LST,
following the elevation of the land and the extent of built-up areas.
A summary of the LST model statistical measurement between the observed LST and
predicted LST is given in Table 3. The result demonstrates that MOD11A1 night has the
greatest satisfied RMSE (2.09
C
) and the best linear relationship (
R2=
0.64), followed
by MYD11A1 night (
R2=
0.48), and MOD11A1 day (
R2=
0.45). On the other hand,
MYD11A1 day is an unsatisfactory model due to the highest RMSE (5.02
C
) and lowest
R2(0.29).
These results confirm the ability of the RF machine learning algorithm, particularly
the MOD11A1 night model, in estimating LST. Conversely, the other predicted LST models
indicate a tendency to overestimate the data.
Table 3. Evaluation of calibrated and validated land surface temperature predictions.
Statistical
Measures
MOD11A1_day
(C/V)
MYD11A1_day
(C/V)
MOD11A1_night
(C/V)
MYD11A1_night
(C/V)
R20.50/0.45 0.51/0.29 0.55/0.64 0.64/0.48
RMSE 2.64/3.79 2.79/5.02 2.03/2.09 1.79/2.57
Min Interval 2.52/3.79 2.66/5.02 1.92/2.08 1.69/2.57
Max Interval 2.75/3.79 2.92/5.03 2.13/2.09 1.89/2.51
MAE 2.02/2.95 2.15/3.88 1.48/1.61 1.33/1.94
MBE 0.05/0.41 0.08/0.58 0.00/0.05 0.19/0.21
Note: C is Calibration, V is Validation.
Figure 3. Importance of selected variables to predict LST for daytime (a,b) and nighttime (c,d).
Appl. Sci. 2024,14, 3969 13 of 27
3.2. Heatwave Detection and Measurement
3.2.1. Ground-Observed Air Temperature Heatwave
Our analysis of annual mean air temperature heatwave indices, considering both
Tmax
and
Tmin
heatwaves, reveals distinct patterns across different areas. In urban settings,
the average yearly occurrence of daytime
Tair
heatwaves ranges between 3.4 and 4.2 days,
compared to 5.2 to 6.8 days in peri-urban regions, and 3.6 to 4.0 days in rural locales.
Notably, the highest frequency of HWF is observed in urban areas at 15.0 to 16.7 days per
year, escalating to 26.4 to 27.1 days in peri-urban regions and 16.9 to 21.0 days in rural
environments. The HWD extends to 4.5 to 5.0 days per year in urban centers, 5.5 to 6.0 days
in peri-urban zones, and 5.9 to 6.5 days in rural areas.
Tair
heatwave analysis reveals that
the highest HWM in urban regions falls between 35.8 and 36.7
C
, 35.5 and 36.8
C
in
the peri-urban area, and 34.3 and 37.6
C
in the rural area. The peak air temperatures of
the HWA are recorded at 37.5–37.9
C
in urban areas, 37.6–38.1
C
in peri-urban zones,
and 36.4–39.7
C
in rural settings. Consequently, the peri-urban area of Pathum Thani is
distinguished by having the most significant occurrence of daytime heatwaves. Moreover,
urban locales, especially Bangkok, persistently display the highest rates and lengths of
heatwave events, and this trend has been progressively intensifying over the years. This
observation highlights the critical need for focused climate adaptation strategies in these
areas to mitigate the impacts of increasing heatwave activity.
Regarding nighttime
Tair
heatwaves, the number of HWN in urban areas ranges from
3.0 to 3.5 days per year, paralleling the daytime patterns observed in other regions. Urban
areas witness the maximum HWF, with 12.3 to 15.5 days per year, surpassing the 18.8 to
22.5 days in peri-urban regions and the 3.6 to 14.3 days in rural areas. For the longest HWD,
it extends from 3.3 to 13.5 days per year in urban settings, 4.6 to 5.4 days in peri-urban
areas, and 3.8 to 5.0 days in rural landscapes. The average cumulative HWM in urban
locales is recorded between 35.8 and 36.7
C
, significantly higher than the 26.3–27.1
C
in
peri-urban areas and 23.7–26.3
C
in rural settings. The peak air temperatures for the HWA
reach 28.5–29.3
C
in urban areas, 27.8–28.8
C
in peri-urban zones, and 25.6–27.4
C
in
rural environments. Thus, nighttime
Tair
heatwave patterns exhibit similarities to their
daytime counterparts, with urban regions being particularly prone to more frequent and
severe events.
3.2.2. Satellite-Based Land Surface Temperature Heatwave
The result reveals the annual total number of daytime HWN, which ranges from two
to nine. Pathum Thani emerges as the most affected region, followed by eastern Bangkok in
terms of HWN prevalence. In terms of the annual HWF, which ranges from 10 to 39 days,
the northern part of Pathum Thani exhibits the highest frequency. Regarding HWD, figures
range from 4 to 11 days, with the rural regions of northern Saraburi experiencing the
longest durations. The highest HWM during these events was recorded in the urban areas
of the Don Muang region of Bangkok, with temperatures ranging from 33
C
to 42
C
.
Additionally, the highest temperatures for the hottest HWA ranged from 35
C
to 45
C
,
also observed in urban areas. To summarize, the annual distribution and characteristics
of daytime heatwaves across different regions indicate that Pathum Thani and Eastern
Bangkok are most prone to experiencing a higher number of HWN. Northern Pathum
Thani endures the highest HWF of these events. Meanwhile, the rural areas of Northern
Saraburi are subjected to the longest HWD. The urban regions, particularly in Don Muang,
Bangkok, are notable for recording the highest HWM and temperatures on the hottest
HWA. The data suggest that heatwaves are more frequent in peri-urban areas, while rural
regions tend to experience longer-lasting heatwaves.
On the other hand, the analysis delineates the annual total number of nighttime HWN,
which ranges from 3 to 12 events. Downtown Bangkok emerges as the area most affected
by heatwave occurrences, closely followed by Northern Pathum Thani. The frequency
of annual nighttime HWF varies from 13 to 62 days per year, with downtown Bangkok
experiencing the highest frequency. Regarding HWD, the analysis shows a range from 4 to
Appl. Sci. 2024,14, 3969 14 of 27
12 days, with the longest observed in downtown Bangkok. The annual mean cumulative
nighttime HWM spans from 21
C
to 27
C
, with the central districts of downtown Bangkok
recording the highest values. The range of the warmest nighttime HWA extends from
21
C
to 28
C
, with the highest temperatures observed in central Bangkok. To conclude,
the annual indices for nighttime heatwave events indicate that downtown Bangkok and
northern Pathum Thani are the most significantly affected regions, both in terms of the
number of nighttime HWN and their HWF. Downtown Bangkok also records the longest
HWD as well as the highest nighttime HWM and warmest nighttime HWA. The analysis
suggests that nighttime heatwaves are more frequent and prolonged compared to their
daytime counterparts, albeit with lower maximum temperatures. Urban areas, especially
downtown Bangkok, are particularly vulnerable to severe nighttime heatwaves.
3.3. Spatial Homogeneity in Correlation Analysis
The degree to which the detected heatwave metrics from
Tair
and LST align is demon-
strated in Table 4. It presents the cumulative ’median’ of Pearson’s correlation coefficient
(
r
) for the 10 meteorological stations and 10 LST grids that match the heatwave indices.
Our LST modeling outperforms the existing MOD11A1 and MYD11A1 products during
daytime heatwaves, notably for HWN at
r=
0.55, 0.62, and HWF at
r=
0.66, 0.71, re-
spectively. It is evident that the derived in situ temperature largely follows the retrieved
LST anomalies. For other indices, their associations are slightly moderate: HWD
r=
0.48,
HWM
r=
0.32 to 0.39 , and HWA
r=
0.35. In contrast to daytime heatwaves, nighttime
heatwaves demonstrate a weaker link between all heatwave characteristics. It is evident
that the derived in situ temperature largely follows the retrieved LST anomalies.
Table 4. Cumulative median Pearson’s correlation coefficient (
r
) for linear relationship between 10
Observed Tair points and 10 valid LST grids, coinciding with heatwave indices at p<0.05.
Data Pearson’s correlation coefficient (r)
HWN HWF HWD HWM HWA
Tmax vs. MOD11A1 (day) 0.55 0.66 0.48 0.32 0.35
Tmax vs. MYD11A1 (day) 0.62 0.71 0.48 0.39 0.40
Tmin vs. MOD11A1 (night) 0.31 0.26 0.10 0.02 0.07
Tmin vs. MYD11A1 (night) 0.36 0.45 0.26 0.08 0.13
Figure 4illustrates the distribution of the correlation coefficient (Pearson’s
r
) at
p<0.05
corresponding to heatwave indices, considering pixel-by-pixel observations of
Tair
and LST. In rural, peri-urban, and urban areas, the link between
Tmax
and MOD11A1 and
Tmax
and MYD11A1 is positively strong, with
r
values larger than 0.60 and up to 0.90, re-
spectively; the highest correlated MOD11A1 result is observed in P07 (located in Ayutthaya
province as a rural context). In addition, the association between
Tmax
and MYD11A1,
particularly in rural regions in Lopburi province, P08 and P09, remains more robust than in
other places (
r=
0.80) for HWN, HWF, and HWD. At
r=
0.60, however, peri-urban and
urban stations are strongly related to HWM and HWA. In a peri-urban area, the connection
between nighttime heatwaves corresponding to
Tmin
and MOD11A1 is highest for HWN
(
r=
0.85) and HWF (
r=
0.75) in Suvarnabhumi Airport (P05). In contrast, HWD shows
the strongest link in the urban region (P03 Bang Na, Bangkok) with a correlation coefficient
of 0.75, while P01 (Khlong Toei, Bangkok) displays the highest HWM (
r=
0.65) and HWA
(
r=
0.62). The
Tmin
against the MYD11A1 product indicates that P05, as the outskirt area,
has the strongest link with all heatwave indices at
r=
0.60 to 0.85, with the exception of
P02 as the urban area, which has a smaller magnitude. Nighttime heatwaves are discovered
to have a negative correlation in more regions than daytime heatwaves, particularly in
rural areas.
Appl. Sci. 2024,14, 3969 15 of 27
Figure 4. The distribution of pixel-wise correlation coefficients (
r
) between observed
Tair
and observed
MODIS-LST;
Tmax
and MOD11A1 Day (a,e,i,m,q),
Tmax
and MYD11A1 Day (b,f,j,n,r),
Tmin
and
MOD11A1 Night (c,g,k,o,s), Tmin and MYD11A1 Night (d,h,l,p,t).
Appl. Sci. 2024,14, 3969 16 of 27
4. Discussion
4.1. Performance Evaluation of Land Surface Temperature Predictive Modeling
Given the inherent limitations of MODIS-LST, which often contains gaps due to
cloud cover and other atmospheric conditions [
47
,
69
], filling these missing data points is
particularly critical in heatwave assessment studies that require daily data inputs rather
than instantaneous or averaged values. Consequently, a crucial step in heatwave assessment
involves the optimization of the dataset during preprocessing, which includes a systematic
treatment of missing values essential for accurate heatwave detection. Selecting appropriate
input variables is crucial before training machine learning models [112]. Our results align
with the findings of [
113
], confirming that LST patterns are not constant and exhibit
seasonal variations. By selecting a more varied set of predictors than those used in previous
studies, our study highlights the critical importance of temporal factors in LST analysis.
It significantly enhances heatwave analysis by exploring the implications of identified
correlations between different variables, particularly in understanding the dynamics of
heatwaves and their impacts.
Our selected methodology, RF, aligns with the findings of [
114
], which underscore
the importance of incorporating a diverse range of environmental and land-use factors in
predicting LST. By employing both temporal and spatial variables as significant predictors
for LST modeling, our approach not only advances our understanding of LST but also
highlights the pronounced impact of temporal variables. This emphasis suggests that
these factors may have a more substantial effect on LST than previous studies. Moreover,
our work builds upon the findings of [
47
,
115
,
116
], which linked LST with factors such
as land cover types, terrain, vegetation, moisture conditions, solar radiation, elevation,
and Julian day. Interestingly, our research has distinctively identified the DOY as the
most significant feature for predicting daytime LST (MOD11A1; R2= 0.28 and MYD11A1;
R2= 0.36
) as shown in Figure 3, owing to its critical role in influencing weather patterns
through its capture of seasonal changes. Similarly, the daily
Tmin
is crucial for nighttime
LST prediction (MOD11A1;
R2
= 0.33 and MYD11A1;
R2
= 0.36), confirmed by the study
of [
117
], which stated that the average of nighttime LST was closest to
Tmin
, addressing
diurnal temperature variations in both urban and non-urban settings.
The outstanding performance of the MOD11A1 night model is supported by its RMSE
of 2.09
C
and high
R2
value of 0.64. These results align with theories suggesting that
certain algorithms possess superior capabilities in processing and analyzing complex envi-
ronmental data, particularly during stable atmospheric conditions at night. Our results are
consistent with those of [
62
], who demonstrated the high spatial–temporal continuity and
relative accuracy of reconstructed nighttime MODIS-LST products across China. However,
temporal feature extraction, which involves incorporating time-related variables such as
day of the year to account for seasonal effects on temperature, is significant. This process is
reflected in the challenges faced by the MYD11A1 day model, which showed data overesti-
mation issues with the highest RMSE (5.02
C
) and the lowest
R2
(0.29). In contrast with
the study of [
118
], it was found that MOD11A1 and MYD11A1 products slightly underesti-
mated daytime LST with an overall absolute bias < 0.9
C
and RMSE < 2.9
C
. The effective
use of the MOD11A1 night model and the challenges associated with the MYD11A1 day
model are consistent with theories in environmental data analysis. This demonstrates a
nuanced understanding of how algorithms perform under various atmospheric conditions.
However, when utilizing LST for heatwave studies, the requirement for daily data is critical
and should be a primary consideration in future research.
The issue of data overestimation in the MYD11A1 day model highlights the practi-
cal challenges encountered when applying theoretical models. This phenomenon can be
attributed to the impact of solar radiation on the thermal infrared signal during daytime,
thereby introducing complexity to the relationship between
Tair
and LST [
116
]. Ref. [
38
]
suggested that utilizing both Terra and Aqua satellites enabled the development of consis-
tent indices suitable for analysis during both day and night. This methodology revealed
that anomalies in LST effectively mirror the climatic trends of the area and reliably sig-
Appl. Sci. 2024,14, 3969 17 of 27
nal the occurrence of heatwaves, especially the more intense ones, which can be further
employed in future studies. Adjusting hyper-parameters in the RF model can lead to
overfitting, particularly when the rules are overly complex and tailored to the training data.
Employing unseen test data and cross-validation methods can help mitigate this risk and
ensure improved performance on new datasets [
119
,
120
]. Furthermore, regarding the use
of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference
Built-Up Index (NDBI) for downscaling the TIR bands, an improvement in performance
accuracy should be further considered in modeling LST [121,122].
4.2. Heatwave Detection, Its Magnitude, and Characteristics
4.2.1. Ground-Observed Air Temperature Assessment
In examining the spatial and temporal patterns of heatwaves, our findings highlight a
distinct trend across various regions. During daytime heatwaves, peri-urban areas such as
Pathum Thani and urban locations like Don Muang in Bangkok are particularly affected,
showing significant increases in HWF, HWD, HWM, and HWA. This phenomenon is
largely attributed to the urban environment, characterized by construction materials such
as concrete, asphalt, and steel, and unique morphological features that inherently possess
heat retention properties. These factors contribute to longer, more frequent, and intense
heatwave conditions [
123
]. The study of [
124
] found that the cumulative hours of ex-
treme heat waves increased significantly with the proportion of urban land and decreased
significantly with the proportion of forested land and water. Additionally, the analysis
highlights the years 1997, 2016, and 2019 as having the highest instances of each heatwave
index, indicating an upward trend in the occurrence of heatwave events. The peri-urban
station in Pathum Thani showed the most significant increases in daytime HWN, HWF,
and HWD, while the most pronounced nighttime HWN increases occurred in rural areas
near Saraburi. This pattern highlights the growing susceptibility of peri-urban areas to
heatwaves, exacerbated by rapid urbanization, diminishing green spaces, and local climate
factors that enhance heatwave sensitivity. This observation is consistent with the research
of [
30
,
125
132
], which associated severe heatwaves with the strongest El Niño–Southern
Oscillation (El Niño) years on record, including 1998, 2010, and 2016. In opposition to
nighttime heatwave, examining heatwave detection through
Tmin
unveils unique patterns
and trends distinct from those associated with
Tmax
. Notably, urban areas such as Bang Na,
Klong Toei, and Don Muang in Bangkok exhibit the most pronounced heatwave activities,
particularly in terms of frequency and duration, while peri-urban regions like Pathum
Thani also experience a high incidence of events. The years 2013 and 2019 are marked as
having significant impacts, with the highest values recorded for various indices of night-
time heatwaves. Across both day and night, an increase in heatwave metrics was observed
in 1997, 2013, 2016, and 2019, coinciding with severe global heatwaves during the most
intense El Niño events recorded so far regarding to [
133
136
]. These observations align
with broader research efforts in the field, such as those by [
26
,
137
], which note significant
climatic shifts impacting heatwave patterns, particularly in urban settings. The findings
underscore the critical importance of understanding regional heatwave trends and their
interactions with biophysical changes, human activities, and land use shifts. The results of
this study are particularly concerning in light of the ongoing expansion of urban areas into
rural landscapes, which could further exacerbate heatwave conditions.
4.2.2. Satellite-Based Land Surface Temperature Assessment
Comparative analysis using satellite data alongside air temperature measurements
further confirms the notable difference, with satellite observations specifically highlighting
the enhanced intensity of nighttime heatwaves in comparison to daytime events across
various metrics. This observation aligns with the findings of [
106
], which suggest that
nighttime and compound heatwaves experience a more pronounced increase in both
frequency and intensity compared to their daytime counterparts. Notably, peri-urban and
rural regions display elevated heatwave metrics consistently. Further, the examination of
Appl. Sci. 2024,14, 3969 18 of 27
annual heatwave patterns at specific sites reveals that daytime heatwave events are more
prevalent and severe in peri-urban areas, such as Pathum Thani and Eastern Bangkok,
with occurrences ranging from 2 to 9 and durations from 10 to 39 days within a year.
In contrast, urban areas, specifically downtown Bangkok, face a higher risk of nighttime
heatwaves, with an annual frequency of 3 to 12 events and durations ranging from 13 to
62 days. Our findings reveal a significant rise in nighttime heatwave number (HWN) and
heatwave amplitude (HWA) trends in urban settings, particularly in the Bang Na and Suan
Luang districts of Eastern Bangkok. These trends suggest a potential amplification of the
urban heat island effect, likely influenced by rapid urban development. The construction
of new residential projects, warehouses, and buildings, alongside proximity to industrial
estates and Suvarnabhumi International Airport, may contribute to this phenomenon.
A study by [
138
] supports this correlation, indicating that a 10% increase in urban built-
up density can lead to a 0.08% to 0.95% rise in HWN. Our study highlights that LST
demonstrates lower amplification for HWM while being higher for HWA compared to air
temperature measurements, particularly in urban settings during daytime heatwaves. Our
findings, in line with [
37
], reveal that comparing air temperatures and satellite-derived
LST data between normal and heatwave years shows a significant increase in daytime air
temperature during heatwaves. For example, the Don Muang region in Bangkok exhibited
the highest annual temperature extremes, with observed HWM fluctuating between 33
C
and 42
C
, and HWA extending from 35
C
to 45
C
. Significantly, the highest mean
HWM of
Tair
was observed in UBKK (Khlong Toei, Bangkok) at 45.5
C
in 1997, and the
highest HWA was recorded in RBCL (Bua Chum Meteorological Station, Lop Buri) at
43.2
C
in 2016. Our findings contrast with the reported effectiveness of LST data in
identifying daytime heatwaves as underscored by [
38
,
139
], demonstrating the strong
correlations between
Tair
and MODIS-LST data. However, ref. [
140
] demonstrates how the
suggested GIS-based methodology may be used to analyze heatwave susceptibility and
effect scenarios in different urban patterns. This result underscores the need for targeted
climate adaptation and resilience strategies, particularly in urban and peri-urban areas,
to address the distinct impacts of daytime and nighttime heatwaves. Therefore, these
findings are crucial for understanding heatwave dynamics, providing essential insights
for anticipating and mitigating heatwave impacts, which are increasing in frequency and
severity due to climate change.
4.3. Spatial Homogeneity in Correlation Analysis between Detected Heatwave Indices from
Ground-Observed Air Temperature and Satellite-Based Land Surface Temperature
Spatial–temporal consistency evaluates the uniformity of identified heatwave patterns
across varied regions and temporal spans. This evaluation is frequently quantified through
the use of correlation coefficients or measures of similarity. By conducting comparisons of
identified heatwave events with ground truth data and evaluating the detection algorithms’
reliability, the study can affirm the integrity of the findings. In the context of satellite-
derived LST for heatwave detection, metrics such as detection accuracy, false alarm rate,
and spatial–temporal consistency are scrutinized [
141
]. Nonetheless, it is imperative
to consider additional factors, including spatial coverage, resolution, and the spectral
properties of the satellite data, to ensure a comprehensive assessment.
Our investigation, employing Pearson’s correlation analysis, reveals a significant
relationship between observed heatwaves based on
Tair
and MODIS-derived LST across
various areas, particularly in its correlation with heatwave indices as detailed in Table 4.
This dual approach is comprehensive, as it covers both the atmospheric temperature
(felt by residents) and the surface temperature (which influences the local microclimate).
During daytime heatwave conditions, the findings are noteworthy, especially in the context
of two specific heatwave characteristics, HWN and HWF, with
r
= 0.55–0.71 indicating a
strong positive correlation. Other aspects, such as HWD, HWM, and HWA, demonstrate a
moderate association. Consistent with the findings of [
139
], the observed match percentages
are relatively high, especially when considering the differences in terms of HWM. Our
Appl. Sci. 2024,14, 3969 19 of 27
results contrast daytime and nighttime heatwave conditions, noting weaker correlations
across all heatwave characteristics during the nighttime, with r= 0.02–0.45.
In particular, the degree that determines the spatial–temporal pairwise correlation is
initially measured as shown in Figure 4. According to the findings of our research, grids
located in rural areas have the potential to form the strongest associations compared to
the other grids in terms of the HWN, HWF, and HWD, as measured by
r=
0.93,
r=
0.94,
and
r=
0.80, respectively. On the other hand, the correlation coefficients for HWM and
HWA are found to be at their maximum in a peri-urban area (Pathum Tani), with values of
r=
0.65 and
r=
0.85, respectively. Overall, this study represents a significant step forward
in our ability to model and understand heatwaves through LST data, especially during
daytime heatwaves, and it is crucial, as it highlights the challenges in modeling nighttime
heatwaves. This agrees with the findings of [
38
], which showed that the proposed LST
index effectively identified heatwaves in the Mediterranean region during the daytime;
however, this correlation was slightly weaker during the nighttime.
The utilization of MODIS-retrieved LST datasets for heatwave mapping, especially
in peri-urban and rural regions with limited meteorological data, marks a significant
advancement over traditional methods. This is particularly beneficial, as it offers the
potential for repetitive imaging at frequent sampling times [
38
]. The strong correlations
in rural and peri-urban areas, coupled with the varied correlations in urban settings,
underscore the intricate interplay between environmental factors and heatwave dynamics
across diverse geographical landscapes. Our approach not only allows for comprehensive
mapping of areas prone to heatwaves but also emphasizes the importance of extensive
geographic analysis and demographic information in understanding heatwave patterns.
Moreover, ref. [
142
] confirmed that both surface imperviousness (SI) and LST could be used
to better understand spatial variation in heat exposures over longer time frames but are
less useful for estimating shorter-term, actual temperature exposures, which can be useful
for public health preparedness during extreme heat events.
4.4. Summary of Comparative Analysis with Existing Related Works
Our LST predictions are consistent with the conclusions drawn in various related
studies that utilize the RF model and consider both temporal and spatial variables (refer
to [
49
,
112
,
113
,
143
]). These studies present compelling findings akin to ours. Moreover, our
methodology, which emphasizes a wide array of predictors, especially temporal factors,
not only corresponds to but also advances upon prior research (refer to [
47
,
114
117
]).
Notably, our analysis identifies the Day of Year (DOY) and minimum daily air temperature
(
Tmin
) as pivotal in predicting daytime and nighttime LST, respectively. However, this
finding contrasts with [
118
], which noted a slight underestimation in LST predictions using
MOD11A1 and MYD11A1 products. Thus, our work highlights the nuanced contributions
of diverse environmental, land-use, and temporal variables to accurate LST prediction,
thereby enhancing model performance and reliability.
In agreement with [
123
], our study highlights the significant impact of
Tair
heatwaves
in regions such as Pathum Thani (suburban) and Bangkok (urban), resulting in these
areas being the most susceptible to global warming. We reveal the link between increased
heatwave metrics (HWF, HWD, HWM, HWA) and significant El Niño events in 1997,
2013, 2016, and 2019, a finding supported by [
133
136
]. Our results also align with the
research by [
26
,
137
], providing further evidence of how climatic shifts influence urban
heatwave patterns, thereby enriching our understanding with a comprehensive, referenced
analysis. Moreover, comparing satellite and in situ air temperature data revealed more
intense nighttime heatwaves, consistent with [
106
], resulting in a pronounced increase in
nighttime heatwave frequency and intensity, which relates to the UHI effect. Contrasting
with [
139
] on the use of LST data, our findings reveal a significant correlation between air
temperature and MODIS-LST data with robust daytime correlations between Tair and LST
in rural (HWN, HWF, HWD,
r>
0.90) and peri-urban (HWM, HWA,
r>
0.65) regions,
challenging traditional perspectives and highlighting the complexity of heatwave analysis.
Appl. Sci. 2024,14, 3969 20 of 27
Furthermore, our integration approach unveils key insights into daytime heatwave
dynamics, with strong correlations in heatwave number and frequency, and moderate
connections in duration, magnitude, and amplitude, both supporting and diverging from
the findings of [
139
]. Specifically, rural areas demonstrate pronounced spatial–temporal
correlations, highlighting the efficacy of our method in detecting heatwave patterns through
LST data—an essential advancement for tackling the challenges of nighttime heatwave
analysis, as noted by [
38
]. By utilizing MODIS-derived LST datasets, our study enhances
the understanding of heatwave episodes in less monitored peri-urban and rural areas,
significantly improving upon traditional methods. This approach, validated by [
142
],
emphasizes the importance of surface imperviousness and LST in mapping the spatial
variability of heat exposure, underlining the intricate interplay between environmental
factors and heatwave dynamics.
Ultimately, this study effectively utilizes the RF model along with temporal and spatial
predictors to enhance LST predictions, providing critical insights into the dynamics of
daytime and nighttime heatwaves. Our findings highlight the vulnerability of urban and
peri-urban areas like Bangkok and Pathum Thani to heatwaves, exacerbated by climatic
shifts such as significant El Niño events and global boiling effects. By deepening our
understanding of these impacts, our research supports the development of informed
climate adaptation strategies, crucial for improving urban and non-urban resilience against
escalating heatwave frequencies and intensities.
5. Conclusions, Perspectives, and Possible Future Works
This study addresses a critical knowledge gap concerning the challenges posed by
heatwaves in Thailand, particularly emphasizing the need for comparative analyses across
urban, peri-urban, and rural settings. By integrating geospatial analysis, remote sens-
ing, and terrestrial data, our innovative methodology meticulously maps heatwave pat-
terns across three socio-economic regions: Bangkok (urban), Pathum Thani (peri-urban),
and Saraburi (rural). The integration of satellite LST data with ground-based observations
has provided a nuanced understanding of heatwave patterns, emphasizing the complex
dynamics that influence urban planning and public health. The methodology introduced
in this study is distinctive in its comprehensive approach to combining various data types
and analytical techniques to improve the precision of heatwave predictions.
Our approach enhances the understanding of the spatial and temporal dynamics
of heatwaves, providing the most detailed collection of air temperature and satellite-
derived heatwave data to date. We effectively utilize the RF model alongside a diverse
array of both temporal and spatial predictors to refine LST predictions, aligning with and
advancing existing research. Key variables, such as the Day of Year and minimum daily
air temperature, have been identified as essential for accurately predicting daytime and
nighttime LST. This robust, accurate, and reliable methodology not only advances current
practices but also demonstrates the potential of machine learning techniques, particularly
the MODIS-MOD11A1 and MODIS-MYD11A1 models, in environmental data analysis
despite challenges such as data gaps and the need for ongoing model refinement.
Our study shows that both air temperature and satellite-derived LST data reveal
significant vulnerabilities to heatwaves in urban and peri-urban areas, particularly in places
like peri-urban Pathum Thani and urban Bangkok, which suffer from prolonged heatwave
durations. We find that these areas, especially downtown Bangkok and Northern Pathum
Thani, experience increasingly frequent and severe heatwaves, predominantly at night.
A unique aspect of our research is the demonstration that LST can reliably serve as a proxy
for heatwave analysis due to its high spatial correlation with ground-based measurements.
This capability is particularly valuable in regions without weather stations. Highlighting
the urgent need for effective climate adaptation strategies, our study leverages satellite
LST data to enhance our understanding and response to heatwave impacts in densely
populated regions where traditional data sources are unavailable.
Appl. Sci. 2024,14, 3969 21 of 27
Furthermore, the limitations posed by the data, including the misalignment of tem-
poral scales between the air temperature and MODIS-derived land surface temperatures,
as well as gaps in the observational records, were significant. Despite these challenges,
the study employed the most accessible datasets available, illustrating the need for im-
proved data collection methods to enhance the reliability of future predictions. Look-
ing ahead, future research should focus on developing comprehensive and interdisci-
plinary strategies to mitigate the impacts of heatwaves. Building on the foundation laid by
this study, further efforts could explore the integration of public health, urban planning,
and community resilience frameworks to develop holistic solutions. Additionally, the em-
ployment of advanced ensemble methods, such as artificial neural networks (ANNs), gene
expression programming (GEP), and gradient boosting models (GBMs), could enhance the
accuracy and reliability of predictions.
In conclusion, this comprehensive study not only corroborates previous findings but
also introduces new insights into the spatial–temporal dynamics of heatwaves, thereby
enriching the scientific dialogue on urban and peri-urban climate resilience. The findings
lay the groundwork for targeted climate adaptation strategies, enabling urban planners
and policymakers to more effectively mitigate the adverse effects of heatwaves. Our contri-
butions to understanding climate resilience are significant, revealing the severe impacts of
heatwaves through a methodological framework that is applicable both in Thailand and
globally. The insights provided here highlight the urgent need for continuous research and
the development of effective adaptation and mitigation strategies to protect communities
from the escalating severity of climate-related challenges. Moreover, the advancements
detailed in this study are vital for informing government policies and decision-making
processes, enhancing resilience against climate extremes in both urban and non-urban
settings, and driving forward efforts towards a sustainable future.
Author Contributions: Conceptualization, T.C., A.T. and H.M.; methodology, T.C., A.T. and H.M.;
software, T.C.; validation, T.C.; formal analysis, T.C.; investigation, T.C., A.T. and H.M.; resources,
T.C.; data curation, T.C.; writing—original draft preparation, T.C.; writing—review and editing, A.T.,
H.M. and T.W.T.; visualization, T.C.; supervision, A.T., H.M. and T.W.T.; funding acquisition, T.C. All
authors have read and agreed to the published version of the manuscript. The authors confirm the
copyright of the figures and tables in the manuscript.
Funding: This research received no external funding.
Acknowledgments: The authors would like to express their deep appreciation to the Land Develop-
ment Department and the Thai Meteorological Department for their invaluable contributions to land
use/land cover and topographic data, and meteorological data, respectively. Acknowledgments are
also extended to Abhishek Koirala for his contributions to the development of scripts for heatwave
detection and to Siwat Kongwarakom for his support in coding the random forest-based land surface
temperature imputation. Finally, the authors also wish to thank the reviewers for their thorough
review of our manuscript and for providing valuable suggestions.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The raw data supporting the conclusions of this article will be made
available by the authors on request.
Conflicts of Interest: The authors declare no conflicts of interest.
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