Land surface temperature variation in relation to vegetation type using MODIS satellite data in...

PLEASE SCROLL DOWN FOR ARTICLE

This article was downloaded by: [Indian Institute of Remote Sensing]On: 10 August 2009Access details: Access Details: [subscription number 908695327]Publisher Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Remote SensingPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713722504

Land surface temperature variation in relation to vegetation type using MODISsatellite data in Gujarat state of IndiaB. R. Parida a; B. Oinam b; N. R. Patel c; N. Sharma d; R. Kandwal e; M. K. Hazarika f

a Max Planck Institute for Meteorology, D-20146 Hamburg, Germany b Department of Hydraulic Engineeringand Water Resources Management, University of Stuttgart, Germany c Indian Institute of Remote Sensing,Dehradun, India d Water Resource Department and Management, IIT-Roorkee, India e Department of CivilEngineering, IIT-Roorkee, India f Geoinformatics Centre, Asian Institute of Technology (AIT), Thailand

Online Publication Date: 01 January 2008

To cite this Article Parida, B. R., Oinam, B., Patel, N. R., Sharma, N., Kandwal, R. and Hazarika, M. K.(2008)'Land surfacetemperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India',International Journal ofRemote Sensing,29:14,4219 — 4235

To link to this Article: DOI: 10.1080/01431160701871096

URL: http://dx.doi.org/10.1080/01431160701871096

Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf

This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.

http://www.informaworld.com/smpp/title~content=t713722504

http://dx.doi.org/10.1080/01431160701871096

http://www.informaworld.com/terms-and-conditions-of-access.pdf

Land surface temperature variation in relation to vegetation type usingMODIS satellite data in Gujarat state of India

B. R. PARIDA{*, B. OINAM{, N. R. PATEL§, N. SHARMA", R. KANDWAL{{and M. K. HAZARIKA{{

{Max Planck Institute for Meteorology, Bundesstraße 53, D-20146 Hamburg, Germany

{Department of Hydraulic Engineering and Water Resources Management, University

of Stuttgart, Germany

§Indian Institute of Remote Sensing, Dehradun, India

"Water Resource Department and Management, IIT-Roorkee, India

{{Department of Civil Engineering, IIT-Roorkee, India

{{Geoinformatics Centre, Asian Institute of Technology (AIT), Thailand

(Received 28 March 2006; in final form 13 December 2007 )

The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided an

improved capability for moderate resolution land surface monitoring and for

studying surface temperature variations. Surface temperature is a key variable in

the surface energy balance. To investigate the temporal variation of surface

temperature in relation to different vegetation types, MODIS data from 2000–04

were used, especially in the reproductive phase of crops (September–October).

The vegetation types used for this study were agriculture in desert areas, rainfed

agriculture, irrigated agriculture, and forest. We found that among the different

vegetation types, the desert-based agriculture showed the highest surface

temperature followed by rainfed agriculture, irrigated agriculture, and forest.

The variation in surface temperature indicates that the climatic variation is

mostly determined by the different types of vegetation cover on the Earth’s

surface rather than rapid climate change attributable to climatic sources. The

mean land surface temperature (LST) and air temperature (Ta) were plotted for

each vegetation type from September to October during 2000 and 2004. Higher

temperatures were observed for each vegetation type in 2000 as compared to 2004

and lower total rainfall was observed in 2000. The relationship between MODIS

LST and Ta measurements from meteorological stations was established and

illustrated that years 2000 and 2004 had a distinct climatic variability within the

time-frame in the study area. In all test sites, the study found that there was a

high correlation (r50.80–0.98) between LST and Ta.

1. Introduction

In the contemporary world, aerospace information gathered by different sensors

and observation missions has become a necessity in various investigations and

application fields. Remote sensing technologies are used for natural resources

management, ecosystem change detection, environment preservation and in many

other significant world problems. Vegetation monitoring is among the priorities of

these investigations. One important parameter that controls the ground thermal

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 29, No. 14, 20 July 2008, 4219–4235

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160701871096

Downloaded By: [Indian Institute of Remote Sensing] At: 07:28 10 August 2009

regime in vegetation areas is the surface heat balance. Satellite remote sensing

provides a means of mapping/monitoring the surface heat balance at the spatial and

temporal resolutions required for regional to continental scale vegetation investiga-

tions. Analysis of past climate variations is a vital part of understanding climate

change, and an important indicator of climate is the surface temperature. The

Intergovernmental Panel on Climate Change (IPCC; Houghton et al. 2001)

emphasized the urgent need for long-term remote sensing-based land surface skin

temperature data in global warming studies to improve the limits of the

conventional 2-m World Meteorological Organization (WMO) surface air

temperature observations. Monitoring of vegetation is improving with the wider

availability of satellite remote sensing data and especially with improved moderate

resolution satellite sensors, such as the Moderate Resolution Imaging

Spectroradiometer (MODIS) instruments onboard the Terra and Aqua platforms.

The MODIS provides information about the Earth’s surface in 36 wavebands

covering the visible, near-infrared, shortwave infrared (SWIR) and thermal ranges.

Several forthcoming spaceborne sensors will have multiple thermal infrared bands

that will enable us to estimate land surface temperature (LST) and emissivity

simultaneously. The MODIS currently has multiple thermal bands in the 3.5–4.2 mm

and the 8–13.5 mm ranges (Salomonson et al. 1989), and the Advanced Spaceborne

Thermal Emission and Reflection Radiometer (ASTER) has five thermal bands in

the 8–12 mm range. The design for land imaging combines and improves upon the

strengths of the existing Advanced Very High Resolution Radiometer (AVHRR)

sensor including finer spatial resolution as well as high-spectral resolution, allowing

significantly better atmospheric profiling, atmospheric correction and more accurate

LST computation.

There is a fundamental difference between climate change and climate

variability. Climate change constitutes a shift in meteorological conditions that

lasts for a long period of time, usually centuries. Climate variability comprises

short-term fluctuations that occur from year to year (Burroughs 2001). The

underlying concept is that global climate changes cause local scale impacts and

that it is not possible to treat variability separately from climate change (Mearns

et al. 1997, Smit et al. 1999, 2000). Assessing the future impact of global

environmental change is an uncertain and important issue. An integrated study of

the human driving forces and implications of environmental change has been

undertaken by the Southeast Asian Regional Committee for START (SARCS) to

look into the issue of rapid environmental change, its causes and consequences, in

Southeast Asia regions (Lebel 1998). The thrust of the study is ‘living with global

change’ in a sustainable way, and focusing on the close interconnection between

sustainable (and unsustainable) development and global change feedback. Many

parts of the Southeast Asia region and the Indian subcontinent have undergone

rapid social, economic and environmental transformations giving rise, more

recently, to economic problems. Industrialization, urbanization and high rates of

population and economic growth, together with the ongoing modifications to

desert, coastal areas and tropical rainforests, make the Southeast Asia region a

‘hot spot’ from both sustainable development and global change perspectives.

Changes in climate will interact with stresses that result from actions to increase

agricultural production, affecting crop yields and productivity in different ways,

depending on the types of agricultural practices and systems in place. Hence, there

is a need to focus on this area of research to assess the effect/impact of the

4220 B. R. Parida et al.


https://www.researchgate.net/publication/225919869_Mean_and_Variance_Change_in_Climate_Scenarios_Methods_Agricultural_Applications_and_Measures_of_Uncertainty?el=1_x_8&enrichId=rgreq-36599f1a45ac80da81ba0e96d0529629-XXX&enrichSource=Y292ZXJQYWdlOzI0ODk3Nzk2MjtBUzoxOTMwNTcxNzQ2MjYzMDRAMTQyMzAzOTgyNTM1OQ==


https://www.researchgate.net/publication/289003717_Climate_change_A_multidisciplinary_approach_second_edition?el=1_x_8&enrichId=rgreq-36599f1a45ac80da81ba0e96d0529629-XXX&enrichSource=Y292ZXJQYWdlOzI0ODk3Nzk2MjtBUzoxOTMwNTcxNzQ2MjYzMDRAMTQyMzAzOTgyNTM1OQ==

dynamics of the LST regime to different land cover features on the Earth’s

surface.

1.1 Satellite-derived LST

LST is generally defined as the skin temperature of the ground. For the bare soil

surface, LST is the soil surface temperature; for dense vegetated ground, LST can

be viewed as the canopy surface temperature of the vegetation; and for sparse

vegetated ground, LST is determined by the temperature of the vegetation

canopy, vegetation body and the soil surface (Qin and Karnieli 1999). LST is a

very useful input for modelling energy balance components and mapping

evapotranspiration (ET). Retrieval of LST using thermal infrared bands of

satellite images is the most effective way of deriving energy balance and ET on a

regional basis. The LST is an important factor controlling most physical, chemical

and biological processes of the Earth. Knowledge of the LST is necessary for

many environmental studies and management activities of the Earth’s surface

resources (Li and Becker 1993). It is one of the key parameters in the physics of

land surface processes on regional and global scales, combining the results of all

surface–atmosphere interactions and energy fluxes between the atmosphere and

the ground (Mannstein 1987).

1.2 Objectives

The aim of this research was to assess the suitability of MODIS data for examining

the temporal variation of surface temperature for different vegetation types between

2000 and 2004. We wanted to determine how the surface temperature dynamics

correlate to local land use types, namely vegetation in desert areas, rainfed

agriculture, irrigated agriculture and forest, as a result of climatic variation within

the time-frame in Gujarat state. To study the climatic variability, this particular time

frame was chosen as a representative test case.

2. Study area and data used

Gujarat state is located in the north-west of India between 20u19 N and 24u79 N

latitude and 68u49 E and 74u49 E longitude (figure 1). The Tropic of Cancer passes

through the northern border of the state. It is bounded by the Arabian Sea on the

west, Pakistan and Rajasthan in the north and north-east, respectively, Madhya

Pradesh in the south-east and Maharashtra in the south. The two deserts, one north

of Kutch and the other between Kutch and the mainland Gujarat, are saline wastes.

Gujarat state covers a total geographical area of 196 024 sq km (75 685 sq m) and

accounts for 6.19% of the total area of the country. It has about 1600 km of

coastline, which is about a third of India’s total coastline and the longest coastline of

all Indian states. This coastline includes the Gulf of Kutch and the Gulf of Cambay.

The state comprises three geographical regions:

N a peninsula, traditionally known as Saurashtra, which is essentially a hilly tract

sprinkled with low mountains;

N Kutch on the north-east is barren and rocky and contains the famous Rann

(desert) of Kutch, the big Rann in the north and the little Rann in the east;

Land surface temperature variation in Gujarat state of India 4221


N the mainland extending from the Rann of Kutch and the Aravalli Hills to the

river Damanganga is on the whole a level plain of alluvial soil.

2.1 Climatic conditions

The climate of Gujarat is moist in the southern districts and dry in the northern

region. The Arabian Sea and the Gulf of Cambay reduce the temperature and render

the climate more pleasant and healthy. The year can be divided into: the winter

season from November to February, the hot season from March to May, the south-

west monsoon season from June to September and the intervening month of

October. The average rainfall in Gujarat varies from 33 to 152 cm. The southern

region of the state has an average rainfall ranging from 76 to 152 cm, the Dangs

district having the highest average of about 190 cm. The northern districts have a

rainfall varying from 51 to 102 cm. The rainfall in the southern highlands of

Saurashtra and the Gulf of Cambay is approximately 63 cm while the other parts of

Saurashtra have a rainfall of less than 63 cm. The semi-desert area of Kutch has a

very low average rainfall. Certain areas in Ahmedabad, Mehsana, Banaskantha,

Panchmahals, Surendranagar, Jamnagar and Kutch districts face chronic water

scarcity conditions for want of adequate rainfall. The air temperature in the

different regions varies with respect to land cover. In table 1 recorded air

temperature measurements are given at 8-day intervals according to the day of

the year (DOY) for four different vegetation types. The minimum and maximum air

temperature and other relevant statistics are shown in table 4 for the years 2000 and

2004. From table 4, it can be inferred that the maximum air temperature is 307 and

Figure 1. Location of study area: Gujarat state, India.



306 K for desert-based agriculture in 2000 and 2004, respectively, and the

corresponding values for forest areas are 305.14 and 301 K.

2.2 Agriculture and different practice types

Agriculture in India is the means of livelihood of almost two-thirds of the work

force in the country. It has always been India’s most important economic sector.

The diversity and variability of climate in India give rise to different types of

agricultural practice in the different states of India. Rainfed agriculture is generally

followed during the south-west monsoon and, where canal facilities are available,

irrigated agriculture is practised. The desert-based agriculture system is used where

scant rainfall is received and the crops grown in these areas require less rainfall. The

scatter of agricultural types used for this study was based on the remote sensing data

land use map (figure 3). The rule-based decision tree (DT) classifier was used for

land use map preparation, using Terra/MODIS surface reflectance 8-day 250 m

resolution satellite data.

2.2.1 The DT algorithm. The use of DTs to classify remotely sensed data has

increased recently. Proponents of the method claim that it has a number of

advantages over the maximum likelihood classification (MLC) and artificial neural

network (ANN) algorithms (Pal and Mather 2003). The DT is computationally fast

and software to implement DTs is readily available over the internet (Pal and

Mather 2003). DTs can be trained quickly, and are rapid in execution in contrast to

neural networks (Gahegan and West 1998). They can be used for feature selection/

reduction as well as for classification purposes (Borak and Strahler 1999). Finally,

an analyst can readily interpret a DT. A DT is not a ‘black box’, like an ANN, the

hidden workings of which are concealed from view. A DT takes as input an object or

a situation described by a set of properties, and outputs a yes/no decision. DTs

therefore represent Boolean functions. A DT is a classification procedure that

recursively partitions a data set into smaller subdivisions based on a set of tests

defined at each node in the tree (Friedl and Brodley 1997). In this framework, a data

set is classified by sequentially subdividing it according to the decision defined by the

tree, and class labels are assigned to each observation according to the leaf node into

Table 1. The air temperature (K) at 8-day intervals for different days of the year (DOY) withrespect to different vegetation types.

Vegetationtype

DOY

241 249 257 265 273 281 289 297

Desert (agriculture)2000 303.02 302.00 302.13 304.11 305.67 305.48 307.00 303.002004 303.86 305.42 301.78 302.95 303.67 302.99 305.00 306.00

Rainfed agriculture2000 299.00 299.00 300.00 301.59 303.52 302.00 305.90 302.262004 298.00 300.00 299.00 299.80 299.20 300.00 304.00 301.50

Irrigated agriculture2000 301.00 302.00 302.64 303.98 305.37 304.57 305.12 305.532004 300.00 304.50 304.00 303.00 303.23 304.27 304.90 303.90

Forest2000 300.81 300.36 300.49 305.14 303.08 301.93 304.00 301.412004 297.00 297.50 296.20 299.00 298.20 299.00 301.00 300.00



https://www.researchgate.net/publication/237715093_Feature_selection_and_land_cover_classification_of_a_MODIS-like_data_set_for_a_semiarid_environment?el=1_x_8&enrichId=rgreq-36599f1a45ac80da81ba0e96d0529629-XXX&enrichSource=Y292ZXJQYWdlOzI0ODk3Nzk2MjtBUzoxOTMwNTcxNzQ2MjYzMDRAMTQyMzAzOTgyNTM1OQ==

https://www.researchgate.net/publication/223439170_An_Assessment_of_the_Effectiveness_of_Decision_Tree_Methods_for_Land_Cover_Classification?el=1_x_8&enrichId=rgreq-36599f1a45ac80da81ba0e96d0529629-XXX&enrichSource=Y292ZXJQYWdlOzI0ODk3Nzk2MjtBUzoxOTMwNTcxNzQ2MjYzMDRAMTQyMzAzOTgyNTM1OQ==



which the observation falls (figure 2). The generalized land use classification was

constructed using the hierarchical rule-based DT supervised classification method in

ENVI (Environmental Visualization) marketed by ITT Corporation. This classifica-

tion was made by taking the actual values of various thresholds of the Normalized

Difference Vegetation Index (ND) image and hence it does not require any training

samples. Classification criteria/rules used for generalized land use classification are

given in table 2.

The south-west monsoon is crucial to the development of Indian agricultural

production. It provides 80% of India’s total precipitation and is important in the

development of its major food and commercial crops such as rice, coarse grains,

pulses, peanuts, soybeans and cotton. The south-west monsoon typically enters the

mainland over Kerala in the first week of June and continues up to September. It

moves northwards to cover the whole of India by mid-July. Planting of the largely

rainfed kharif (monsoon season) crops, which include rice, sorghum, corn, millet,

peanut, soybean and cotton, begins after the monsoon is firmly established in the

state and continues throughout July and in early August. According to the local

crop calendar the sowing starts after the onset of the monsoon and varies

according to the arrival of the monsoon. Rice is largely grown under rainfed

conditions during July and August and is harvested during November and

December, whereas other rainfed crops are sown during July and harvesting

Figure 2. A decision tree classifier. Each box is a node at which tests (T) are applied torecursively split the data into successively smaller groups. The labels A, B and C at each leafnode refer to the class label assigned to each observation (Source: Friedl and Brodley 1997).

Table 2. The criteria/rules used for generalized land use classification.

Rules Class

ND.0.74 Forest0.74,ND.0.46 Irrigated crops0.46,ND.0.20 Rainfed crops0.20,ND.0.15 Fallow land0.15,ND.0.05 Bare soils0.05,ND.0.001 Salt pansND,20.001 Water

ND is the 8-day composite MODIS-derived ND image during 15 October 2004 (DOY 281).



depends upon the length of the growing period in the state. The western and

central parts of Gujarat are dominated by the cotton crop, which lasts up to theend of November to mid-December. In brief, the critical growth phase of the

major crops falls in the months of September and October. Thus, depending upon

the rate of precipitation, the general agriculture practised in the state is rainfed

agriculture, irrigated agriculture and desert-based agriculture. Agriculture in

Gujarat forms a vital sector of the state’s economy. It provides the required food

grains for the state’s population and raw materials for most of the agrobased

industries. Unsuitable climatic conditions in some parts and rocky terrain have

limited the area suitable for cultivation. The difficulty of drainage in coastal areasand in the two Ranns has made a large part of the state agriculturally

unproductive.

2.3 LST data

The satellite data used for this study were MODIS LSTs and the product was the 8-

day 1 km MOD11A2, averaging LSTs in the daily product MOD11A1 over 8 days.

The 8-day composites were the product downloaded from the EOS data gateway for

the period from September to October from 2000 to 2004. This time series represents

the reproductive phase of the crops (the end part of the kharif season). The LSTs inMOD11A1 were retrieved by the generalized split-window algorithm (Wan and

Dozier 1996). The emissivities in bands 31 and 32 were estimated by the

classification-based emissivity method (Snyder et al. 1998) according to land cover

types in the pixel determined by the input data in quarterly land cover (MOD12Q1)

and daily snow cover (MOD10_L2) products.

2.4 Surface reflectance data

The surface reflectance 8-day composite data on 15 October 2004 (product

MOD09Q1) were downloaded from the EOS data gateway and used for land use/land cover mapping in the state. The surface reflectance product is an atmo-

spherically corrected product generated from the MODIS level 1B radiances data

(Vermote and Vermeulen 1999). The compositing technique is based on the

minimum-blue criterion, which selects the clearest conditions over the period. This

product is a composite of eight consecutive daily 250 m images.

Using the surface reflectance bands, we computed ND for land use/land cover

mapping:

ND~ NI{Rð Þ= NIzRð Þ ð1Þ

where NI and R are the reflectance in the near-infrared and red bands of the MODIS

satellite data. ND is a good indicator of green biomass, leaf area index, and patterns

of production (Thenkabail et al. 2004). ND is the most commonly used vegetation

index and it varies from + 1 to 21.

2.5 Air temperature data

The daily air temperature data, namely the minimum and maximum temperatures,

were collected for the four regions in the years 2000 and 2004 (Gujarat Agriculture

University, Anand). The daily data sets were converted to 8-day composite data forcomparison with the LSTs. The air temperature data, especially those obtained in

September and October, were used for statistical analysis.



https://www.researchgate.net/publication/238203190_Classification-Based_Emissivity_for_Land_Surface_Temperature_Measurement_from_Space?el=1_x_8&enrichId=rgreq-36599f1a45ac80da81ba0e96d0529629-XXX&enrichSource=Y292ZXJQYWdlOzI0ODk3Nzk2MjtBUzoxOTMwNTcxNzQ2MjYzMDRAMTQyMzAzOTgyNTM1OQ==

3. Methodology

To examine the temporal variation of surface temperature in relation to different

vegetation types, the MODIS data from 2000 to 2004 were used, especially in the

end part of the kharif season. The vegetation types used for this study were

agriculture in desert areas, rainfed agriculture, irrigated agriculture and forest.

Different vegetation types were located based on the land use map (figure 3) and

then the MODIS LSTs for these areas were extracted. Table 3 shows the geographic

coordinates of the vegetation type, where the surface temperature value has been

extracted using a 565 window size. Initially, we extracted the LST pixel values using

a 363 window size but at later stage we found that a 565 window size gave better

results in terms of variation in pixel values and representation of wider areas. The air

temperatures from the meteorological stations were used to evaluate the results of

LST variation among the different vegetation types. The air temperature during the

months of September and October were correlated with the corresponding LST by

taking the nearest meteorological station. The 8-day composite mean air

Figure 3. The land use and land cover map for Gujarat state.



temperature was computed from the daily maximum air temperature for each

station, and only September and October monthly air temperatures were used for

the analysis. The meteorological stations as named in table 3 were near to the four

types of vegetation. Bhuj-Rudramat station was taken as the example for agriculture

in the desert area (the northern Gujarat/Kutch region), which is mainly situated in

the drought prone area. Similarly, the other stations, Rajkot, Ahmedabad and

Gandevi, were taken as examples for rainfed agriculture, irrigated agriculture and

forest, respectively. This study also exemplifies the rainfall pattern for each

vegetation type in order to determine the effect of rainfall variation on land surface

characteristics and local land use types. The rainfall measured in the corresponding

meteorological stations was compared with the respective vegetation types. For the

analysis of rainfall variation, the total rainfall from 22–43 standard meteorological

weeks (June to October) was used.

4. Results and discussion

In this section we present some results to prove the potential value of LST variation

in relation to vegetation types. In addition, evaluation of LST is presented by

comparing MODIS LST with the surface air temperature in a particular time-frame

as a representative test case for study of the climatic variability. The rainfall

variation in different types of vegetation was also analysed to determine the climatic

variability within a certain time-frame, that is 2000 and 2004. By contrast, a global

climatic change study requires long-term data to determine the global environment

change, such as global warming and its impact on local-scale land use types. The

spatial distribution of surface temperature is shown in the map for the years 2000

and 2004 for the entire state and it was found that the surface temperature changes

at different times, and is highly dependent on the climatic conditions and different

land use types. The variation also assessed whether the climatic change was within

the normal range or was due to changes in vegetation types.

Cloud contamination in satellite images is a significant obstacle to land surface

studies. Undetected clouds distort the reflectivity of the land surface and

consequently develop into an additional source of error. Clouds in satellite images

must therefore be identified precisely prior to any further analysis. It was noted that

the few images obtained during the months of July and August were covered with

cloud in the study area, which presents a difficulty in the study for the whole kharif

season. Hence the present study focused only on the months of September to

October instead of an earlier attempt from June to October.

Table 3. The vegetation types, reference location and meteorological stations.

Type of vegetation Location Referenced location Meteorological station

Desert agriculture 23u50940.180 N71u09914.450 E

Northern Gujarat Bhuj-Rudramat

Rainfed agriculture 22u21923.430 N71u22904.310 E

Saurashtra/western partof Gujarat

Rajkot

Irrigated agriculture 23u00949.850 N72u55936.960 E

Middle/eastern Gujarat Ahmedabad

Forest 20u48932.650 N73u31912.660 E

Southern Gujarat Gandevi



4.1 Spatial variation of MODIS LST

Figure 4 shows that the surface temperature varies with respect to vegetation cover

and at different times. The surface temperature in the forest area shows the least

temperature variation in the year during both 2000 and 2004. The surface

temperature in irrigated agriculture and rainfed agriculture is higher than the forest

cover. The southern district of the state shows a lower surface temperature than the

other parts of the state. The northern part of the state shows a higher surface

temperature, and is a desert-prone area. As a result the agriculture practised in the

desert area shows a higher temperature. In general, the temperature pattern varies

from month to month in different years. In most of the areas the LSTs in

MOD11A2.A2000257 (21 September 2000) were generally in the range 36.85–

46.85uC (but some were .46.85uC) whereas the LSTs in MOD11A2.A2004281 (15

October 2004) were in the range 31.85–41.85uC. This figure shows that 2000 was

hotter than 2004. This result can be compared with the actual ground conditions,

that is 2000 was a drought year whereas 2004 was normal for the Gujarat state. The

white patch in figure 4 indicates no data because of cloud cover in the data set.

4.2 Analysis of surface temperature variations with MODIS time series data

To determine the temporal variation of the surface temperature, surface temperature

was plotted at 8-day intervals during September to October for the years 2000–2004.

The surface temperature variation is shown in figure 5 with respect to the different

vegetation types. In the study area the vegetation areas taken for the analysis were

forest, agriculture in desert area, rainfed agriculture and irrigated agriculture.

Multitemporal images were processed for each vegetation type to obtain basic

information for understanding the dynamics. Surface temperature patterns derived

from this study show that the various vegetation types have different responses to

temperature, changing from the lower part to the upper part of the state.

The surface temperature dynamics study was performed in relation to the

vegetation cover for a particular time frame (2000–04) for Gujarat state. For the

type of agriculture practised in the desert area, the surface temperature varied from

308 to 321 K. From day 241 to day 297, the temperature curves for the years 2000,

Figure 4. The spatial variation in surface temperature in MOD11A2.A2000257 (September)and MOD11A2.A2004281 (October).



2001 and 2002 follow a similar pattern to those in 2003 and 2004. At the beginning

of the three years, the curve is above the 2003 and 2004 curves except for 241–249

DOY for 2004. This shows that the beginning of the three years had a hotter climate

or higher temperature as compared to the years 2003 and 2004 across the 8 days

difference. On the contrary, the 2003 and 2004 curves are lower, indicating a lower

surface temperature. This is because of the non-drought year and 2000–03 were

drought years in the study area.

In rainfed agriculture the surface temperature varied from 302 to 321 K, which

indicates that the temperature is lower than the desert-based agriculture area. One

distinct feature we can note is that the 2004 curve is lower, which indicates a lower

temperature as compared to the other year. Overall, all of this class show a lower

surface temperature pattern as compared to desert-based agriculture.

In irrigated agriculture the surface temperature varied from 299 to 315 K, which

indicates that it was cooler. We noted that the surface temperature variation in

irrigated agriculture was lower than the desert-based agriculture and the rainfed

agriculture. The 2000, 2001 and 2002 curves show similar surface temperature

patterns to the desert-based agriculture and the only difference is related to the lower

LST values, although the variation curve is similar. 2003 and 2004 show lower curves,

indicating lower temperatures as compared to the beginning of the three years.

In the forest-type vegetation the surface temperature varied from 295.6 to 306 K,

which indicates the lowest temperature as compared to all other vegetation types for

all the years across the DOY. However, all of the curves do not change across the

year distinctly as compared to the other vegetation classes. We note that the 2003

and 2004 curves are in the lower part of the graph representing lower LSTs.

4.3 Statistical analysis between LST and air temperature (Ta)

In the following we summarize the results of the comparison between the MODIS

LST daily global 1 km product from Terra and air/near-surface ground temperature

measurements from meteorological stations. From the results described above, we

found that the years 2000 and 2004 show a distinct variation in terms of LST for each

Figure 5. The surface temperature variations for different vegetation types during 2000–04.




vegetation type due to the climatic variability in the study area. Hence we illustrate the

temperature variation statistical result for 2000 as higher LST and 2004 as lower LST

in table 4. In this study we have also computed the minimum, maximum, mean,

standard deviation (SD) and correlation coefficient (r) for LST and air temperature

from September to October by taking 8-day composite values.

The results from the Bhuj-Rudramat site in northern Gujarat show good

agreement between LST values extracted from MODIS pixels on these sites and

those measured at the stations in 2000 and 2004. The maximum LST was 321 K (307

Ta) during 2000 and 314.4 K (306 Ta) during 2004. The lower SD shows the higher

consistency of the result. A higher standard deviation is often interpreted as higher

volatility. In comparison, a lower standard deviation would most likely be an

indicator of stability. The most consistent values are usually the set of numbers with

the lowest standard deviation. This site is located in a desert area where agriculture

shows a lower yield compared to other parts of Gujarat state. In this station the

weather conditions are very erratic and scanty rainfall is received. The correlation

coefficient determines the relationship between two properties. The surface

temperature of MODIS and air temperature show high correlation: r50.88 (2000)

and 0.83 (2004).

At Rajkot, where rainfed agriculture is predominant, the maximum LST was

321.8 K (305 Ta) during 2000 and 313 K (304 Ta) during 2004. The mean

temperatures observed were 314.6 K in 2000 and 308.6 K in 2004, which are lower

than those in northern Gujarat. At Ahmedabad the maximum LST was 315 K (305

Ta) during 2000 and 307 K (304.9 Ta) during 2004. However, the mean temperature

difference in irrigated agriculture (i.e. at Ahmedabad station) was 5.94 and 1.38 K

for 2000 and 2004, respectively. This difference in mean temperature is due to the

fact that in 2000 the state was severely affected by drought, which also affected the

irrigated zone of agriculture. As a result, the temperature difference in the irrigated

zone shows a positive instead of a negative value. At Gandevi the maximum LST

was 305.9 K (305 Ta) during 2000 and 302.2 K (301 Ta) during 2004. Thus, the

results reveal that the mean LST gives a lower value, which is in decreasing order

from desert-based agriculture followed by rainfed agriculture, irrigated agriculture

and forest, respectively. The surface temperature obtained by MODIS and Ta show

good correlation for Rajkot, Ahmedabad and Gandevi.

A correlation coefficient (r) was derived using the two temperatures. As these

relationships vary with different land types and topography, separate correlation

coefficients were determined for each vegetation type. The surface temperature

variation mainly depends on the surface vegetation cover, topographical properties,

soil moisture and atmospheric conditions.

The mean surface temperature and air temperature were computed for each

vegetation type from September to October during 2000 and 2004. These were plotted

in figure 6, which reveals that the year 2000 showed a higher surface temperature for

each vegetation type as compared to the year 2004. This figure also reveals that the

desert-based agriculture crops show the highest temperature and forest the lowest

surface temperature for both years. In addition, the rainfed agriculture shows a higher

surface temperature than irrigated agriculture. This could be due to the higher

moisture content in irrigated agriculture than rainfed agriculture. The mean air

temperature was also plotted and the results show that there was a higher temperature

during 2000 than in 2004. The mean LST (Ts) and Ta show a similar pattern of

temperature variation. The difference in temperature (Ts–Ta) varies with time of day,



Table 4. Relationship between air temperatures measured at meteorological stations and land surface temperatures retrieved from Terra-MODIS.

Minimum temperature(K)

Maximum temperature(K) Mean temperature (K) SD

Correlation coefficient(R)

Desert (agriculture) (Northern) – Bhuj-Rudramat2000 LST 314.80 321.10 317.83 2.24 0.88 **

Air temperature 302.00 307.00 304.05 1.832004 LST 307.96 314.42 311.53 2.19 0.83*

Air temperature 301.78 306.00 303.96 1.42Rainfed agriculture (Saurashtra) – Rajkot2000 LST 302.00 321.88 314.60 7.19 0.84**

Air temperature 299.00 305.90 301.66 2.362004 LST 305.56 313.10 308.63 2.99 0.84**

Air temperature 298.00 304.00 300.19 1.84Irrigated agriculture (Middle) – Ahmedabad2000 LST 302.00 315.26 309.72 4.91 0.84**

Air temperature 301.00 305.53 303.78 1.702004 LST 301.38 307.38 304.85 1.97 0.80*

Air temperature 300.00 304.90 303.48 1.54Forest (Southern) – Gandevi2000 LST 300.00 305.90 302.83 2.46 0.82*

Air temperature 300.36 305.14 302.15 1.752004 LST 297.16 302.24 299.80 1.83 0.98**

Air temperature 296.20 301.00 298.49 1.59

*Correlation is significant at the 0.05 level (two-tailed).**Correlation is significant at the 0.01 level (two-tailed).

La

nd

surfa

cetem

pera

ture

varia

tion

inG

uja

rat

state

of

Ind

ia4

23

1


and in this paper (Ts–Ta) was evaluated for each type of vegetation by taking the mean

temperature from September to October. In desert-based agriculture, the temperature

difference was 13.77 K in 2000 and 7.57 K in 2004. The corresponding values for

rainfed agriculture were 12.95 and 8.44 K, for irrigated agriculture they were 5.94 and

1.38 K, and for forest, they were 0.68 and 1.31 K in 2000 and 2004, respectively.

According to Moran et al. (1994) the hypothetical vegetation index/temperature (VIT)

trapezoid shape indicates that the temperature difference (Ts–Ta) for dry bare soil

could be 20uC and negative for well water vegetation, which was further computed

from an experimental result for alfalfa crop. In this study the mean temperature

difference for irrigated agriculture was different from the hypothetical VIT

trapezoidal shape (Moran et al. 1994). However, it was observed that the mean

temperature difference for 2000 was higher because of the drought event and hence

even the crops are under the irrigated zone, where there was still a scarcity of water

supply. This may also be due to the generalized irrigated agriculture practices, where

the crops are not always well watered and do not reach the potential level of water as a

result of the irregular water supply in the irrigated zone of the state. Moreover, the

literature on the temperature difference (Ts–Ta) with respect to crops is sparse, and

future studies are needed using remote sensing data as well as ground validation.

Nevertheless, the above results indicate that MODIS-derived LST has the potential to

monitor the temperature regime and to study the climatic variability within a

particular time-frame. Figure 7 suggests that the total rainfall (June–October) was

lower during 2000 compared with 2004 in all the meteorological sites. In fact, the

desert area (Bhuj-Rudramat) received much lower rainfall as compared to the forest

area (Gandevi).

5. Summary and conclusions

Vegetation is an important component of the global climate system through its

control of energy partitioned over substantial portions of the land surface.

Knowledge of the LST is important to a range of issues and themes in earth

sciences such as global environmental change, human–environment interactions

Figure 6. The mean LST and Ta for September to October for different vegetation types.



and, more specifically, vegetation types. The MODIS provides a new and improved

capability for terrestrial satellite remote sensing aimed at meeting the needs of global

change research and providing tools for moderate resolution land surface

monitoring. The present research found that the surface temperature varies with

vegetation type. Among the different vegetation types, the agriculture practised in

the desert area shows the highest surface temperature, followed by rainfed

agriculture, irrigated agriculture and forest. The variation in surface temperature

indicates that the climatic variation is mostly determined by vegetation cover on the

Earth’s surface rather than a rapid climate change attributable to climatic sources in

the study area. The mean LST and air temperature were computed for each

vegetation type from September to October during 2000 and 2004. Higher

temperatures were observed for each vegetation type in 2000 as compared to 2004

and lower total rainfall (June–October) was observed in 2000 as compared to 2004.

The relationship between the MODIS land surface temperature daily global 1 km

product from Terra and air temperature measurements from meteorological stations

was established and illustrates that years 2000 and 2004 have a distinct climatic

variability within a particular time-frame in the study area. For all the

meteorological sites, the study found that there was a high correlation (r50.80–

0.98) between LST and Ta.

The present study has demonstrated that there is great scope for studying the

surface temperature variations with respect to different vegetation types using

MODIS satellite data and this could be extended in microclimatic level analysis. The

response of different crop types to changing surface temperature in the environment

is a scientific challenge for food security and trade as a whole and merits further in-

depth research.

Acknowledgements

We thank Dr V. H. Prasad, Indian Institute of Remote Sensing, Dehradun for

providing the facilities during the project, and the Department of Agrometeorology

Figure 7. The total rainfall at different meteorological stations for June to October during2000 and 2004.



(AAU, Anand), Gujarat, for sharing meteorological data. We also thank the

anonymous reviewers for their constructive comments and suggestions.

ReferencesBORAK, J.S. and STRAHLER, A.H., 1999, Feature selection and land cover classification of a

MODIS-like data set for semi-arid environment. International Journal of Remote

Sensing, 20, pp. 919–938.

BURROUGHS, W.J., 2001, Climate Change: A Multidisciplinary Approach (Cambridge:

Cambridge University Press).

FRIEDL, M.A. and BRODLEY, C.E., 1997, Decision tree classification of land cover from

remotely sensed data. Remote Sensing of Environment, 61, pp. 399–409.

GAHEGAN, M. and WEST, G., 1998, The classification of complex data sets: an operational

comparison of artificial neural networks and decision tree classifiers. In Proceedings of

the 3rd International Conference on Geocomputation, 17–19 September 1998,

University of Bristol, UK. Available online at http://divcom.otago.ac.nz/SIRC/

GeoComp/GeoComp9861/gc_61.htm (accessed 10 April 2003).

HOUGHTON, J.T., DING, Y., GRIGGS, D.J., NOGUER, M., LINDEN, P.J., and XIAOSU, D. (Eds),

2001, Climate Change 2001: The Scientific Basis (Cambridge: Cambridge University

Press).

LEBEL, L., 1998, Environmental change in Southeast Asia: causes and consequences. GCTE

Core Project Office, CSIRO Division of Wildlife and Ecology.

LI, Z.L. and BECKER, F., 1993, Feasibility of land surface temperature and emissivity

determination from AVHRR data. Remote Sensing of Environment, 43, pp. 67–85.

MANNSTEIN, H., 1987, Surface energy budget, surface temperature and thermal inertia. In

Remote Sensing Applications in Meteorology and Climatology, R.A. Vaughan and D.

Reidel (Eds). NATO Advanced Study Institutes Series. Series C, Mathematical and

Physical Sciences, Vol. 201, pp. 391–410 (Dordrecht, the Netherlands: D. Reidel

Publishing Company).

MEARNS, L.O., ROSENZWEIG, C. and GOLDBERG, R., 1997, Mean and variance change in

climate scenarios: methods, agricultural applications, and measures of uncertainty.

Climatic Change, 35, pp. 367–396.

MORAN, M.S., CLARKE, T.R., INOUE, Y. and VIDAL, A., 1994, Estimating crop water deficit

using the relation between surface-air temperature and spectral vegetation index.

Remote Sensing of Environment, 49, pp. 246–263.

PAL, M. and MATHER, P.M., 2003, An assessment of the effectiveness of decision tree

methods for land cover classification. Remote Sensing of Environment, 86, pp.

554–565.

QIN, Z. and KARNIELI, A., 1999, Progress in the remote sensing of land surface temperature

and ground emissivity using NOAA-AVHRR data. International Journal of Remote

Sensing, 20, pp. 2367–2393.

SALOMONSON, V., BARNES, W., MAYMON, P., MONTGOMERY, H. and OSTROW, H., 1989,

MODIS: advanced facility instrument for studies of the Earth as a system. IEEE

Transactions on Geoscience and Remote Sensing, 27, pp. 145–153.

SMIT, B., BURTON, I., KLEIN, R.J.T. and STREET, R., 1999, The science of adaptation: a

framework for assessment. Mitigation and Adaptation Strategies for Global Change, 4,

pp. 199–213.

SMIT, B., BURTON, I., KLEIN, R.J.T. and WANDEL, J., 2000, An anatomy of adaptation to

climate change and variability. Climatic Change, 45, pp. 223–251.

SNYDER, W.C., WAN, Z., ZHANG, Y. and FENG, Y.Z., 1998, Classification-based emissivity

for land surface temperature measurement from space. International Journal of

Remote Sensing, 19, pp. 2753–2774.

THENKABAIL, P.S., GAMAGE, M.S.D.N. and SMAKHTIN, V.U., 2004, The use of remote

sensing data for drought assessment and monitoring in Southwest Asia. International

















Water Management Institute (IWMI) Report, Colombo. Available online at:

www.iwmi.cgiar.org/pubs/pub085/RR85.pdf (accessed on 10 April 2007).

VERMOTE, E.F. and VERMEULEN, A., 1999, Atmospheric correction algorithm: spectral

reflectances (MOD09), Version 4.0, April 1999. MODIS algorithm technical

background document.

WAN, Z. and DOZIER, J., 1996, A generalized split-window algorithm for retrieving land-

surface temperature from space. IEEE Transactions on Geoscience and Remote

Sensing, 34, pp. 892–905.



Land surface temperature variation in relation to vegetation type using MODIS satellite data in...

Documents

Transcript of Land surface temperature variation in relation to vegetation type using MODIS satellite data in...