derivation of near surface air temperature for exploring ...derivation of near surface air...

Derivation of Near Surface Air Temperature for Exploring

Microclimatic Conditions: A Case Study of Madurai

District, India

Arijita Bhakta1, S. Mahesh Kumar2

Department of Geography, University of Madras, Guindy, Chennai- 600 025.

[email protected]

[email protected]

Abstract- Near Surface Air Temperature (NSAT) plays a major role in understanding many of the environmental processes. Accurate

near surface air temperature analysis is an important parameter in climatological, meteorological, ecological and agricultural

applications. The meteorological stations collect near surface air temperature data at regular time frequency, but the meteorological

stations are not sufficient for estimating local regions NSAT with significant accuracy levels. Whereas using Remote Sensing satellite

data, pixel based estimations of NSAT can be made for any region of our interest. A better understanding of surface and air temperature

(i.e.) Ts~Ta relation is thus required for characterizing micro climate idiosyncrasies to solve real world localized problems. NSAT is not

only influenced by topography but also other complex terrain parameters. The concept proposed in this paper is based on Vegetation

Index-Temperature (VIT) Model for the estimation of NSAT from LST. The approach combines the vegetation indices and the surface

temperature measurements. It has been found out that, LST and NDVI are the two powerful predictors in evaluating and modelling

NSAT.

Keywords: Land Surface Temperature(LST), Near Surface Air Temperature(NSAT), NDVI, Regression, VIT Model.

I. INTRODUCTION

Near Surface Air temperature (NSAT) is an important key parameter to indirectly describe weather and climate. Knowledge of

near surface air temperature is an indispensable factor which is equally necessary to assess water and other climatic parameters

which are responsible for plant growth. To summarize, accurate NSAT is a key factor for environmental, climatological, public

health and ecological applications. NSAT also helps in prediction of heat transport near the ground. It is a basic climatic parameter

that governs the moisture and energy changes between the Land surface and Atmosphere. (Sun et al, 2005). NSAT is a critical

variable which plays an important role in micro climate of a terrain. It is the most important component of global climate change

which is highly affected by local anthropogenic activities (Guan et al, 2013). The estimation of NSAT deals with the preliminary

study of Land Surface Temperature(LST) derived from satellite data. There are two methods in which NSAT can be calculated,

one being the physical and other the empirical approach. The physical approach takes into account the aerodynamic processes as

well as the status of soil, vegetation and heat budget. In this approach, it is incredibly difficult to gather the data as its availability

is only theoretical (Sun et al,2005). The empirical method deals with the advantage of Remote Sensing data and the use of

Geographic Information System for analysis. This method helps the indirect calculation and estimation of the NSAT from LST.

JASC: Journal of Applied Science and Computations

Volume VI, Issue III, March/2019

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The low density and the uneven distribution of weather stations and traditional ground based observations cannot accurately

capture spatial distribution to provide local conditions of air temperature (Ta). This paper deals with the estimation of NSAT from

LST using the Vegetation Index-Temperature method. The method is best suited to adopt for deriving near surface air temperature,

as the leaf temperature acts as a function of air temperature, maximum illumination, mean wind velocity and net solar radiation.

It has been found that, leaf and air temperature results tend to be similar under over cast sky conditions while in clear sky the leaf

temperature exceeded the air temperature results (Salisbury and Spomer, 1964).The Vegetation Index-Temperature (VIT) method

also proved that surface temperatures of dense vegetation shows very less variations and are relatively close to near surface air

temperature (Gillies et al, 1997).Thus, the analogy for the analysis is that, leaf temperature at vegetation surface has been equal

to the air temperature at 2m height of ground surface(Suleiman and Crago,2002). The paper adopts the contextual method for

analysis of NSAT using Landsat-8 OLI data (Czajkowski et al, 1997, Prihodko and Goward 1997 and Prince et al, 1998).

II. THEORITICAL BACKGROUND

Solar radiation, water vapour, thermal conductivity, surface capacity, relief and albedo are some of the parameters responsible for

the change in the earth’s temperature. There lies a very basic relationship between Vegetation Index – Temperature in the VIT

Method. Near Surface Air temperature largely depends on the heating of the underlying characteristics of the ground surface. It

has been found from studies that there is a significant manifestation in the thermal characterises among the soil and vegetation

which can be derived from satellite sensed images. It is stated that, vegetation plays an important role in controlling the pattern

of NSAT of a region. At high radiation, the leaves near the ground shows high temperature values (Larcher 1994). The green

foliage of high grown plants in an enclosed area shows a good insulation layer between soil and atmosphere (Geiger et al, 1995.)

This foliage- temperature relationship can be largely used for defining the Crop Water Stress Index (CWSI).

Land Surface Temperature (Ts) and Normalized Difference Vegetation Index (NDVI) are the parameters characterized for

Thermal Emission and Vegetation cover types. The authors stated a linear regression relationship between Surface temperature

and vegetation cover (Goward and Hope. 1989, Gillies and Carlson 1995). The basic assumption derived from these studies are,

(a)the thick canopy vegetation over the surface area determines the near surface air temperature measured by the thermal sensor,

(b) the dense canopy vegetation can be stated as the maximum vegetation cover which is used in the equation.

Thus, keeping the detailed background of the working of Thermal sensors for the vegetation cover and the surface temperature

following the VIT trapezoid, this paper adopts the procedure for the derivation of NSAT for the study Area.



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III. STUDY AREA

The study area taken for this research paper is Madurai district, situated in the state of Tamil Nadu, India. The study area ranges

from about 9.56ON to 10.30ON and 77.46OE to 78.47OE with a total administrative area of around 3742 square kilometre. The

region tends to have an average elevation of 101 meters above the mean sea level and lies in the flat and fertile plain of river

Vaigai equally dissecting the district into two parts.

The study area enjoys tropical hot summer from April to June whereas the months of December to February seems to be pleasant.

The region receives monsoon rain by Southwest and Northeast from June to September and October to December respectively.

This district is largely an agriculture based region, in which 76.35% of total area are of rain fed cultivable lands. Red Loamy and

Black soils are the parent soils of this region. Dry mixed Deciduous and Moist Mixed Deciduous are the main types of Vegetation

that are found in this region. Scrubs and agricultural lands cover other knot of land cover.

Fig 1. Location of study area.

IV. DATA USED

Landsat-8 OLI data of 17th April 2017 (Path & Row - 143/53 from USGS Earth Explorer) is used for the calculation of LST. It

has a spatial resolution of 30 m. The derived LST is used to Estimate the NSAT for the study area.



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V. METHODOLOGY

The methodology adopted for this research paper has been an empirical approach. The Landsat 8 data has been used to calculate

the Surface Temperature using parameters like Normalized Difference Vegetation Index(NDVI), Land Surface Emissivity(e) and

Proportion of Vegetation(PV). Finally, NSAT was derived from estimated LST.

A. Estimation of LST

The following steps are used for calculation of LST:

1) For calculation of Brightness Temperature:

T = …………………………..…..... (i)

2) For calculation of Top of Reflectance

Lλ = MLQcal + AL…………………………...…. (ii)

3) For calculation of NDVI

(NIR-RED)/ (NIR+RED)...………………………..….... (iii)

4) For calculation of PV

PV=[(NDVI-NDVImin)/ (NDVImax - NDVImin)]2……….. (iv)

5) For calculation of Land Surface Emissivity

(e) = 0.004*PV+0.986……………………………..... (v)

6) For calculating LST

LST=BT/1+W*(BT/p) *ln (e)…………………………….... (vi)

B. Estimation of NSAT

From the derived surface temperature, the temperature on top of vegetation (i.e.) canopy temperature is calculated, which is

approximated to NSAT. The Normalized Difference Vegetation Index and the Surface Temperature are used as the main input

parameters for this estimation. After the calculation of surface temperature, sample points of 400 meters’ interval have been

created using Arc Map tools. The sample points are overlaid on both LST and NDVI rasters. The corresponding values are

extracted and assigned to their respective points. Linear Regression between the NDVI and LST has been done in order to derive

the slope(a) and intercept(b) variables.

C. Linear Regression

Linear Regression is based on the mathematical concept of a straight line equation (i.e.) y= ax+b. The corresponding equation for

this study is,

Ta=a(NDVI)+b… ………................…. (vii)

K2

ln + 1

K1

L1



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The equation derived based on the values of NDVI and LST is y= -34.67x+44.516 (Fig 2). It was referred that an agricultural

regression line shows less variation than a vegetated parameter (Czajkowski et al,1997).

Fig 2. Regression between NDVI and LST

The regression has been done using PAST software, from which a and b values are calculated and assigned to the derived equation.

The value of NDVI has been substituted with NDVImax. It’s because, NDVImax was found to be an effective parameter for cell

based estimation of canopy characteristics when compared to NDVI. NDVImax was obtained using Focal Statistics tool in ArcGIS

software with the selection of stipulated kernel size of 3x3 matrix.

[Tc=a (NDVImax) +b] ≈≈≈≈Ta(OC)……….. (viii)



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This canopy temperature (Tc) is used as an equivalent to near surface air temperature (Ta) following VIT method.

Fig 3. Methodology Flowchart

VI. RESULT AND DISCUSSION:

It has been found out that the study is achievable in relating Ts and Ta by creating a linear regression model between LST and

NDVI. Though the procedure deriving this model can be same for all other but due to the vast variations of terrain, climate, time,

land cover/ land use and other geographical parameters, the result will be varying for each linear model that is achieved.

From this empirical study, Madurai tends to have a surface temperature in range of 24 to 45 degree celsius in the month of

April’2017. NSAT results were perfectly related with the derived surface temperature results which ranges between 26 to 48

degree celsius.

A boxplot is a statistical way of analyzing the distribution of data based on a five number summary i.e. minimum, first quartile(Q1),

median or second quartile(Q2), third quartile(Q3) and maximum. Boxplots helps to define the outliers and its values. The box plot

has been generated using Geoda Software. Results from the boxplot says that there lies a high dispersion in LST values than

NSAT. It was calculated that the range of 36 degrees was found to be the average temperature for NSAT while it was 2 degrees

more for LST. There were some extreme values in both the analysis which were plotted as outliers.

≈≈≈≈

NEAR SURFACE

AIR TEMPERATURE

NDVI

LANDSAT-8

PRE-PROCESSING

LINEAR

REGRESSION LST

DERIVED EQUATION NDVI (max)

CANOPY TEMPERATURE



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BOX PLOT

Fig 4. Box plot of LST Fig 5. Box plot of NSAT

It has been depicted from the study that mainly central parts have high LST than NSAT. This shows a change in the microclimatic

conditions of this region. The eastern part of the study area shows climatic changes when compared with NSAT. Land cover

influence on estimating Ta is time dependent and conditional as the satellite data when captured plays an important role.

Top

Whisker

Upper

Quartile(Q3)

Bottom

Whisker

Median(Q2)

Lower

Quartile(Q1)

Outliers

Outlier



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Fig 6. NDVI(max) – Madurai

Fig 7. LST-Madurai.



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Fig 8. NSAT-Madurai

VII. CONCLUSION:

The research can be concluded as a successful study and a new scientific, practical based method to estimate NSAT from remote

sensing data. This is mainly because of the remote sensing method bases on the hypothesis that temperature of the air layer existing

directly above the vegetation layer approximates the foliage temperature. The area-wide mapping and monitoring of key

parameters of air temperature is indispensable for the spatial modelling of meteorological and hydrological processes. This is the

crucial factor why remote sensing methods are investigated for potential for air temperature estimation. In this application, the

contextual method seems to be most promising. The Western and Central part tends to have low Ta and as well as Ts, due to the

existence of dense Sirumalai Reserved Forest. The microclimatic conditions depict high range of change of temperature over the

study area which is predominantly and perfectly based on the land use and land cover of the study area. Madurai is divided on the

basis of its main river, which tends to have an effect on the relation of LST and NSAT. As this is a total empirical and satellite

based study, the estimated results can be validated by relating it with an ample number of samples collected from field for better

accuracy. NSAT data can be collected using Laser Thermometer for ground truth verification study of any region.

ACKNOWLEDGEMENT

The authors would like to acknowledge Defence Research and Development Organization (DRDO, New Delhi) for providing

financial assistance for the research project entitled,” Terrain Evaluation with 3D Visualization for Defence Operations in Infantry

Deployment and Column Movement. This support gave the scholars an opportunity to work on this research paper. We would also

like to thank Dr. N Sivagnanam (Research Consultant) for his constant guidance.



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