LAND SURFACE TEMPERATURE AS AN INDICATOR OF URBAN HEAT ISLAND EFFECT: A GOOGLE EARTH ENGINE BASED WEB-APP

Page 1

LAND SURFACE TEMPERATURE AS AN INDICATOR OF URBAN HEAT ISLAND

EFFECT: A GOOGLE EARTH ENGINE BASED WEB-APP

∗Abhinav Galodhaa , Sharad Kumar Guptab

a School of Interdisciplinary Studies (SIRe) Indian Institute of Technology, IIT Delhi, (abhinavgalohda@gmail.com)

b Punjab Remote Sensing Center, Ludhiana

Commission VI, WG VI/4

KEY WORDS: Surface urban heat island; Google Earth Engine; Land Surface Temperature; MODIS; Landsat; Sustain-

able Development Goals

ABSTRACT:

At least 2 billion urban occupants will be concentrated in Asia and Africa, amounting to 70% of the global population by

2050. This rapid urbanization has caused an innate effect on the ecology and environment, which further results in intense

temperature variations in urban and rural areas, especially in India. According to a recent IPCC report, 8 out of the 15

hottest cities in the world are situated in India. The rising industrial work, construction activities, type of material used

for construction, and other factors have reduced thermal cooling and created temperature imbalance, thereby creating

a vicious effect called ”urban heat island” (UHI) or ”surface urban heat island” (SUHI). Several researchers have also

related it with climate change due to their contribution to the greenhouse effect and global warming. In this study, we

have particularly emphasized northern India, including Punjab, Rajasthan, Haryana, and Delhi. We created a Google

Earth Engine (GEE) based Web-App to assess the UHI intensity over the past 15 years (2003 – 2018). We are using

Moderate Resolution Imaging Spectroradiometer (MODIS) images, Landsat 5, 7, and 8 data for studying UHI. The land

surface temperature (LST) based UHI intensity (day and night time) will be available for major metropolitan cities with

their respective clusters. With feasibility in SUHI monitoring, we can address an increasing need for resilient, sustainable,

and safe urban planning of our cities as portrayed under the Sustainable Development Goals (SDG 11 highlighted by

United Nations).

1. INTRODUCTION

Climate change, increase in urban-rural population, rise in

population density, and poverty are few out of a huge lot

leading to challenges gripping the world. The rise in tem-

perature both air flux temperature and land surface tem-

perature have been contributing factors in climate changes

[Mustafa et al., 2020]. Several studies have shown that

better social, economic and livelihood prospects lead to

unprecedented urban migration, which may subsequently

lead to temperature rise and cities behave as urban heat

islands (UHI) [Li et al., 2013, Sheng et al., 2017].

India currently stands second just shy of China to be-

come the most populous nation by 2027. Massive urban

migration has become visible for a better life and work

prospects in both formal and informal sectors, which may

have caused changes in land-use patterns and fluctuations

in regional temperature. A direct relationship can be es-

tablished between the two [Chen and Zhang, 2017]. Re-

search shows the positive effects of the presence of green-

cover, agriculture, and water-bodies helping to regulate

the UHI effect, and simultaneously the negative conse-

quences of built-up area and uncovered landcover accel-

erate UHI at an alarming rate [Amiri et al., 2009, Chen

and Zhang, 2017, Song et al., 2014].

Remote sensing is very beneficial for UHI and LST stud-

ies. Multispectral images obtained from Landsat series

sensors i.e. TM (thematic mapper), ETM+ (enhanced the-

matic mapper), and TIRS (thermal infraRed sensor) are

∗Corresponding author

beneficial for estimating LST and determining the appro-

priateness of 30m spatial resolution for UHI studies [Guha

et al., 2018]. The Landsat series is available at a global

scale at 30m spatial resolution but with poorer temporal

resolution at 16 days. For carrying out UHI and LST stud-

ies, this research uses thermal infrared bands of Landsat-5

and Landsat-8, available at 30 m spatial resolutions [So-

brino et al., 2004, Jiménez-Muñoz et al., 2014]. Landsat

series with help of new emerging methods such as mono-

window algorithm [Qin et al., 2001], single-channel algo-

rithm [Jiménez-Muñoz and Sobrino, 2003, Jiménez-Muñoz

and Sobrino, 2009] have made UHI and LST estimations

easy.

Global surface UHI explorer app1, developed by Yale

University helps to monitor UHI intensities at a global

scale using Google Earth Engine (GEE). The UHI dataset

was based on a simplified urban-extent algorithm (SUE)

using MODIS Terra and Aqua dataset at 1 km spatial

resolution [Chakraborty and Lee, 2019]. The app shows

the annual daytime and nighttime temperatures of 10,000

big cities around the globe and contains a year-wise slider

from 2003 until 2018. It also generates two charts for the

selected cluster, where the first one displays the change

in the UHI intensity from 2003 to 2018, the second chart

shows the monthly variation of the UHI intensity derived

from 15 years of MODIS data.

In this paper we have developed a GEE-based Webapp

for determining LST and analyzing the UHI effect at 30 m

spatial resolution using Landsat 5 and Landsat 8 images.

1https://yceo.users.earthengine.app/view/uhimap

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.

https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-57-2021 | © Author(s) 2021. CC BY 4.0 License.

Page 2

2. STUDY AREA AND DATA

UHI and LST estimation was carried out for Punjab state

in the northern part of India. Punjab is bordered by the

mountainous region and union territory of Jammu and

Kashmir (J & K) to the north, Chandigarh city to the

east, Himachal Pradesh to the northeast, Haryana to the

southeast, and Rajasthan to the southwest. The state adds

an area of 50,362 sq. kilometers, accounting for 1.53% of

India’s total geographical area. The state lies between

29.54°N to 32.57°N latitudes and 73.88°E to 76.94°E lon-

gitudes.

In this study, we have used thermal infrared (TIR)

bands available at 120m and 100m from Landsat 5 and

Landsat-8 satellites respectively. Emerging technologies

and algorithms were used or LST estimations. We have

also used MODIS (or Moderate Resolution Imaging Spec-

troradiometer) on-board Terra and Aqua satellites with

global coverage of Earth at a temporal resolution of 1 day.

For the development and validation of a global, interac-

tive Earth system, MODIS is playing a vital role thus able

to predict global change accurately enough to assist pol-

icymakers in making sound decisions concerning the pro-

tection of our environment. Even at coarser resolutions

such as 250m, 500m, and 1km due to global and day-night

time coverage, MODIS datasets are extremely fruitful in

UHI and LST estimations (https://modis.gsfc.nasa.gov/

about/).

Figure 1: Study Area

3. METHODOLOGY

Google Earth Engine (GEE) has petabytes of freely avail-

able datasets from the United States Geological Survey

(USGS). This research uses various datasets available in

GEE such as Landsat-5, Landsat-8, MODIS etc. For com-

putation of LST from Landsat-8, either of the two bands

10 or 11 can be used in split-window method; however

band 11 has some quality issues and large calibration con-

straint. Hence, band 10 is preferable for thermal studies in

determining brightness temperature. The digital number

(DN) image can be converted to top of atmosphere (TOA)

reflectance, which can be further used to compute surface

reflectance (SR). The SR is used to determine normalized

difference vegetation index (NDVI) using NIR and RED

band of the images. SR reflectance data is readily available

from GEE datasets derived from Land Surface Reflectance

Code (LaSRC) where the coastal band is used for aerosol

inversion, and MODIS data for auxiliary atmospheric and

atmospheric correction performed with radiative transfer

model [Vermote and Kotchenova, 2008]. Quality assess-

ment band (BQA), can be used to retrieve cloud coverage

masking, which includes cloud shadowing for which a func-

tion was created in GEE [Ermida et al., 2020]. The NDVI

equation is mentioned as:

NDVI =

NIR−Red

NIR+Red

(1)

where

RED = Red Band (Band 4)

NIR = Near Infrared Band (Band 5)

NDVI = Normalized Difference Vegetation Index (NDVI)

3.1 Surface Emission

Using the NDVI thresholds, min, max values, surface emis-

sivity (ε) was estimated using the equations given as the

NDVI thresholds method [Sobrino et al., 2004, Sobrino et

al., 2001]. Also, the fractional vegetation (Fv), of each

pixel was determined from the NDVI threshold informa-

tion using the following equation [Carlson and Ripley, 1997]:

Fv =

NDVI−NDVIbare

NDVIveg −NDVIbare

(2)

where

NDVIbare = NDVI (Completely Bare)

NDVIveg = NDVI (Fully Vegetated)

NDVI = Normalized Difference Vegetation Index (NDVI)

Fv = Fractional Vegetative Cover

Using the Fv parameter, (ε) can be determined using equa-

tion [Guha et al., 2018]:

ε = 0.004×Fv +0.986

(3)

where

ε = Emissivity Factor

The (ε) is derived from Landsat bands as follows [Ermida

et al., 2020]:

• Fv is derived from ASTER using the NDVI equation.

• Using original ASTER emissivity, bare ground emis-

sivity and corresponding Fv with a prescribed value of

εb,veg=0.99

• ASTER emissivity for bare ground can be used for

each TIR band 10,11 of Landsat-8 with spectral fixing.

• Corresponding NDVI values for Fv determination.

• Fractional vegetative cover is fundamentally used for

calculating emissivity in each TIR band combination.

3.2 LST Estimation

Using reflectance values from thermal band 10 of Landsat-

8 TIRS, spectral radiance was determined using [Carnahan

and Larson, 1990]:

Lλ = 0.0003342×DN+0.1

(4)

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.

https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-57-2021 | © Author(s) 2021. CC BY 4.0 License.

Page 3

where

Lλ = Spectral Radiance

DN = Decimal Number

Brightness temperature can be determined using the equa-

tion [Guha et al., 2018]:

TB =

ln(K1/Lλ )+1

(5)

where

Lλ = Spectral Radiance

TB = Brightness Temperature (K)

K1 = 774.89

K2 = 1321.08

Eventually, LST was estimated using the equation (Weng,

Lu, & Schubring, 2004):

LST =

1+(λσTB/¯hc)

(6)

where

LST = Land Surface Temperature

TB = Brightness Temperature (K)

λ = Wavelength

¯h = Planck’s Constant

c = Velocity of light (m/sec)

3.3 CHIRPS Daily: Atmospheric Data

Climate Hazards Group InfraRed Precipitation Station (CHIRPS)

gridded data is a 30+ years dataset representing quasi-

global rainfall/precipitation. It incorporates 0.05° arc res-

olution satellite images along with in-situ/field-based sta-

tion data to create tile/grid-based rainfall time series for

trend analysis and seasonal drought forecasting. The CHIRPS

dataset is available at a global scale from 1981 - until

now. Precipitation band is the only band available with

its raster values as a function of mm/day.

3.4 Landuse Landcover

Landuse classification for the given study area can be car-

ried out mainly into 4 classes i.e. Agriculture, Forest,

Urban-city, and Waterbody. There are two methods to

carry out classification i.e. unsupervised (without the pres-

ence of class labels) and supervised (with the presence of

class labels). General steps to carry out a supervised based

classification are (developers.google.com/earth-engine/guides/

classification):

• Store training data, store class labels, identify specific

properties of each class label as a numeric value to

define part of predictors.

• Combine all these labels with the specific property as

a feature collection.

• Identify or create a classifier, hyper-tune its parame-

ters.

• Train the classifier with set parameters with defined

training data.

• Classify image or feature collection with defined la-

bels.

• Validate with test dataset, create error-matrix.

• Compute the overall accuracy and classified map.

• Iterate the process after tuning the hyper-parameters

until desired overall accuracy isn’t obtained.

With property storing class labels and properties stor-

ing predictor variables, training data is considered a fea-

ture collection. Class labels need to be continuous, start-

ing from 0 and stored as integers. When it is necessary

to convert class values to continuous integers, use remap()

function. The prediction labels have to be numeric values.

Validation, testing, and/or training dataset can be

made from multiple sources. Preparation of training dataset

can be intensely carried out in GEE, with the help of ge-

ometry drawing tool. Alternatively, a predefined training

set can be imported from a GEE as an asset. A classi-

fier can be defined from one of the constructor tabs as an

ee.Classifier and can be trained using the classifier.train()

option. The current research uses a statistical machine in-

telligence and learning engine (SMILE) Classification and

Regression Trees (CART) classifier [Breiman et al., 1984]

to easily predict more than two classes.

4. WORKFLOW

The entire workflow can be segregated into 3 phases

Figure 2: Workflow

starting with input where we have Landsat-8 time series

data, layer stacked. Cloud-cover mast at less than 10%

cover. In the Data processing section, here I am showing

single snippets using Landsat 8 imagery. I am determin-

ing the vegetation cover with normalized difference vege-

tation index. Using the thermal band of Landsat 8 image

collection and brightness temperature determine the land

surface temperature and emissivity factor. For land cover

classification I am using the smile cart supervised classifi-

cation algorithm. Using the climate hazards group infrared

precipitation station data abbreviated to CHIRPS which

gives a global daily precipitation data at 0.05 arc resolution

I determine the precipitation. Also, MODIS TERRA and

Aqua dataset is used for surface heat Island estimation. In

the output section, I am showing the Indian outline map

and my highlighted study area Punjab state. in the bot-

tom corner, shown is the LST time series chart variation

for the year 2016.

MODIS Terra and Aqua datasets are used to witness

and analyze the annual summer, winter day-time, and

nighttime variations across the years from 2003 until 2018.

Overlaying city-wide clusters for the study area to identify

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.

https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-57-2021 | © Author(s) 2021. CC BY 4.0 License.

Page 4

the UHI and LST variations. Annual max and min for sea-

sonal variation across the day-time and nighttime are also

added as additional drop-down options. The advantages

are the availability of Terra as a daytime sensor and Aqua

as a nighttime sensor for assessing thermal temperature

variations. The temporal resolution of 1 day is also an

additional advantage. The drawbacks, however, include

coarser resolution of 1km and not taking into account the

other factors for example precipitation variations, land-

cover changes, vegetation cover which have a tremendous

impact in controlling the UHI effects [Chakraborty and

Lee, 2019]. These factors were covered in the proposed

research using Landsat-8 OLI/TIRS (LS), CHIRPS (pre-

cipitation) datasets.

LS series have been beneficial to cover the UHI and

LST estimates from 2013 - until now, at 30m fine resolu-

tion with one drawback being the poor temporal resolu-

tion of 16 days. CHIRPS daily global precipitation since

1981 until now provides data at fine resolution for annual

precipitation in (mm/hr) was incorporated for the study.

Landuse landcover was done for the entire study area using

the SMILE CART algorithm. GEE user interface platform

(https://code.earthengine.google.com/) is used for the im-

plementation of all these factors. Finally, a GEE-based

web app is also developed to monitor the LST estimation

which soon will be available to use for the general public.

5. RESULTS AND DISCUSSION

Now for the results section, in the figure-4 top left cor-

Figure 3: LST, Land Cover Classified Image, Precipitation

(mm/hr)

ner, we have the precipitation map using CHIRPS data set

measured in mm/day. The red portion signifies Higher pre-

cipitation cover followed by mild precipitation in the green

region and lower precipitation in the blue region. In the

top right corner, the land cover classification was carried

out using a smile cart supervised algorithm, there are four

classes water body agriculture City, and forest. the classi-

fication results overlap and match with the ground truth

labels used. the water body is signified with blue color,

agriculture is signified with the red region as Punjab state

is a hub for agriculture yield. The city is one with yellow

color and forest with a dark green cover. In the bottom

corner, I have the LST map estimated from the brightness

temperature equation, the blue color predominates in the

Northern and North-Eastern regions of Punjab signifying

the lower temperatures. The urban settlement has red and

yellowish red color along with the South Western region

signifying the higher temperature.

In figure-5 of the result top left corner signifies images

for Landsat 8 image collection visualized as true color

composite (TCC). The top left corner signifies the veg-

etation cover determined using normalized difference with

index dark green color showcases forest cover, light green

color is predominantly agriculture and Orange color is

for non-vegetation cover. The bottom left corner indi-

cates the thermal band of Landsat-8, the light blue por-

tion has a lower temperature compared to White and light

green portions which indicate high temperature. Emis-

sivity shown in the bottom right corner determined from

the LST brightness temperature equation indicates with

higher temperatures in the red and yellow region and lower

temperature is indicated by light green and darker green

color which is dominated by forest cover and Agriculture.

Figure 4: Results (Stacked Landsat -8 TCC, Vegetation

Cover (NDVI), Thermal Band)

In figure-6 here, annual precipitation as a function of

mm/day for the year 2020 is shown as a time series for

this study area month of March till April we have average

precipitation during months of May and June due to Lu

condition and absence of tropical winds, the temperature

is much higher thus precipitation is lower. the month of

mid-July till early October there is a sudden spike in pre-

cipitation due to the onset of southwest monsoon winds.

December to January slight rainfall is occurring due to the

presence of North-Western disturbance.

In figure-7 here, the global urban heat island explorer

app is created by the center of earth observation, Yale Uni-

versity (https://yceo.

yale.edu/research/global-surface-uhi-explorer). In, yellow

is a cluster of 10,000 cities available globally. To find

the surface UHI intensity it has been implemented using

MODIS Terra Aqua data set from the year 2003 to the

year 2018. The options include annual day and night time,

summer day and night time, winter day and night time.

Finally, figure-10 shows the interface on GEE Explorer

App that showcases the algorithm to create land surface

temperature estimation with Landsat-8 OLI/TIRS sen-

sors. This tool maps land surface temperature in the Pun-

jab region from 2013-onwards using a brightness temper-

ature (BT) parameter from Landsat-8, Vegetation cover

(NDVI), Landcover classification using Smile Cart Clas-

sification derived from Landsat imagery. The landcover

classification had an overall accuracy of 90.17%. One can

use the tools below to explore changes in LST estimation,

vegetation cover, land cover changes, and precipitation

changes. The Dropdown menu has been made available

to the user to filter the satellite images for different dates.

Selection of images as part of image collection can be car-

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.

Page 5

Figure 5: Results (CHIRPS , Precipitation (mm/day))

In figure-8 here, LST variation for Chandigarh city is

shown for the state of Punjab, the annual temperature

variation can be seen for the year 2018 where light glow

signifies the annual day temperature varying less than -1.5

degree Celsius while light brown region indicates annual

day temperature varying from 0 to 1.5 degree Celsius.

Figure 6: Results (MODIS Terra- Aqua Dataset for LST

Estimation

ried out as individual scenes can be overlaid on the map

layer. A visualization drop menu is provided to choose the

band compositions to visualize images on map-layer. LST

classified images can also be added as an overlay option.

6. CONCLUSION

In this paper, we have shown the UHI and LST estima-

tion for the state of Punjab, India using both Landsat-8

OLI/TIRS sensor datasets and MODIS Terra and Aqua

datasets. We can conclude that MODIS Terra and Aqua

datasets are crucial for LST and UHI determination due

to mainly two reasons. Firstly, availability of 1km global

daily data and secondly, availability of both daytime and

nighttime mean, max and min temperature variations for

10,000 cluster cities mainly can be considered the relevant

reasons to use MODIS data. Using Landsat-8 OLI/TIRS

sensor-based imagery is beneficial mainly for its fine spa-

tial resolution at 30m and optimizing with other factors

(NDVI, Fv, (ε) and Thermal information (Band 10,11)).

Punjab is an agricultural hub, thus the NDVI values are

more than 0 throughout the year ranging between (0.1-

1.0). The annual precipitation derived using the CHIRPS

dataset from 1981-on wards, shows that Punjab is predom-

inately affected by SW and NW trade winds. The LST,

thermal information also shed the same concept that the

rural regions in SE Punjab (Abhor) along Rajasthan state

have shown a large heatwave condition due to the pres-

ence of loo during summers, lack of vegetation cover, and

water scarcity. Emissivity variations for SE Punjab are

Figure 7: LST Estimation for Chandigarh City

In figure-9 here, this graph on the left side Red Line in-

dicates annual daytime and the blue line indicates annual

night-time UHI intensity in degree Celsius, from the pe-

riod 2003 to 2018, while the right corner graph shows the

seasonal variability of UHI intensity between monthly day

and night time UHI intensities in degree Celsius for the

year 2018.

Figure 8: Annual and Monthly UHI Intensity

Figure 9: GEE Explorer Web-App for LST Estimation:

Demonstration

higher than in other areas. Precipitation (mm/hr) annu-

ally has receded throughout the years. These, all factors

are reasoning’s behind the high-temperature surge. Incor-

porating drought parameters can help establish the rising

UHI and Surface Heat Island (SHI) effects.

ACKNOWLEDGEMENTS

The authors are grateful to the Google Earth Engine server

for implementation of the code https://code.earthengine.

google.com/ and to the GEE-developers community plat-

form for resolving doubts (https://developers.google.com/

earth-engine).

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.

Page 6

REFERENCES

Amiri, R., Weng, Q., Alimohammadi, A. and Alavipanah,

S. K., 2009. Spatial–temporal dynamics of land surface

temperature in relation to fractional vegetation cover and

land use/cover in the tabriz urban area, iran. Remote

sensing of environment 113(12), pp. 2606–2617.

Breiman, L., Friedman, J., Olshen, R. and Stone, C., 1984.

Cart. Classification and Regression Trees, Wadsworth and

Brooks/Cole, Monterey, CA.

Carlson, T. N. and Ripley, D. A., 1997. On the relation

between ndvi, fractional vegetation cover, and leaf area

index. Remote sensing of Environment 62(3), pp. 241–252.

Carnahan, W. H. and Larson, R. C., 1990. An analysis of

an urban heat sink. Remote sensing of Environment 33(1),

pp. 65–71.

Chakraborty, T. and Lee, X., 2019. A simplified urban-

extent algorithm to characterize surface urban heat islands

on a global scale and examine vegetation control on their

spatiotemporal variability. International Journal of Ap-

plied Earth Observation and Geoinformation 74, pp. 269–

280.

Chen, X. and Zhang, Y., 2017. Impacts of urban surface

characteristics on spatiotemporal pattern of land surface

temperature in kunming of china. Sustainable Cities and

Society 32, pp. 87–99.

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M. and

Trigo, I. F., 2020. Google earth engine open-source code

for land surface temperature estimation from the landsat

series. Remote Sensing 12(9), pp. 1471.

Guha, S., Govil, H., Dey, A. and Gill, N., 2018. Analyti-

cal study of land surface temperature with ndvi and ndbi

using landsat 8 oli and tirs data in florence and naples

city, italy. European Journal of Remote Sensing 51(1),

pp. 667–678.

Jiménez-Muñoz, J. C. and Sobrino, J. A., 2003. A gen-

eralized single-channel method for retrieving land surface

temperature from remote sensing data. Journal of geo-

physical research: atmospheres.

Jiménez-Muñoz, J. C. and Sobrino, J. A., 2009. A single-

channel algorithm for land-surface temperature retrieval

from aster data. IEEE Geoscience and Remote Sensing

Letters 7(1), pp. 176–179.

Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mat-

tar, C. and Cristóbal, J., 2014. Land surface temperature

retrieval methods from landsat-8 thermal infrared sensor

data. IEEE Geoscience and remote sensing letters 11(10),

pp. 1840–1843.

Li, X., Zhou, W. and Ouyang, Z., 2013. Relationship

between land surface temperature and spatial pattern of

greenspace: What are the effects of spatial resolution?

Landscape and Urban Planning 114, pp. 1–8.

Mustafa, E. K., Co, Y., Liu, G., Kaloop, M. R., Beshr,

A. A., Zarzoura, F. and Sadek, M., 2020. Study for pre-

dicting land surface temperature (lst) using landsat data:

a comparison of four algorithms. Advances in Civil Engi-

neering.

Qin, Z., Karnieli, A. and Berliner, P., 2001. A mono-

window algorithm for retrieving land surface temperature

from landsat tm data and its application to the israel-

egypt border region. International journal of remote sens-

ing 22(18), pp. 3719–3746.

Sheng, L., Tang, X., You, H., Gu, Q. and Hu, H., 2017.

Comparison of the urban heat island intensity quantified

by using air temperature and landsat land surface tem-

perature in hangzhou, china. Ecological Indicators 72,

pp. 738–746.

Sobrino, J. A., Jiménez-Muñoz, J. C. and Paolini, L., 2004.

Land surface temperature retrieval from landsat tm 5. Re-

mote Sensing of environment 90(4), pp. 434–440.

Sobrino, J., Raissouni, N. and Li, Z.-L., 2001. A compar-

ative study of land surface emissivity retrieval from noaa

data. Remote Sensing of Environment 75(2), pp. 256–266.

Song, J., Du, S., Feng, X. and Guo, L., 2014. The rela-

tionships between landscape compositions and land surface

temperature: Quantifying their resolution sensitivity with

spatial regression models. Landscape and Urban Planning

123, pp. 145–157.

Vermote, E. F. and Kotchenova, S., 2008. Atmospheric

correction for the monitoring of land surfaces. Journal of

Geophysical Research: Atmospheres.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIV-M-3-2021

ASPRS 2021 Annual Conference, 29 March–2 April 2021, virtual

This contribution has been peer-reviewed.