Enumerating and Modelling the Seasonal alterations of Surface Urban Heat and Cool Island: A Case Study over Indian Cities

Abstract

The present study has been carried out to analyze the seasonal variation of the Urban Heat and Cool Island over the nine developing cities of India. The magnitude of urban heat/cool island and vegetation gradient (∆NDVI) were measured from the daytime satellite datasets. Results of this study show that during the pre-monsoon (March to May) season, the maximum magnitude of the Surface Urban Heat Island (SUHI) was experienced over Kolhapur city, whereas, in the winter, the highest intensity of SUHI was noticed over Pune city. Subsequently, outcomes also depict that the changes in ∆NDVI restrain the pre-monsoon means and the seasonal alterations in SUHI magnitude. However, during the winter (November to February) season, it is controlled by the temperature–vegetation conditions of the rural areas. For pre-monsoon and seasonal changes in SUHI, with the aid of ∆NDVI and the surface temperature of the urban area, regression equations were fitted for pre-monsoon and seasonal changes in SUHI, which explains nearly 90% of SUHI variation. Similarly, the variation of SUHI has been modeled for winter, which elucidates up to 84% of SUHI discrepancy. The study reveals that, on a seasonal scale, a decrement of 0.1 in seasonal ∆NDVI leads to an increase in the seasonal intensity of SUHI by 1.74 °C, which is quite a significant augmentation.

1. Introduction

Urbanization is the process where an increasing percentage of a population lives in cities and suburban areas. This process is often linked to industrialization and modernization, as many people shift from rural to urban areas [1]. These urban areas generate their climatic domain dissimilar from their surrounding rural area as they have different land surface properties, partitions of surface energy balance, urban geometry, soil moisture, vehicle density, etc. [2]. This disparity between the urban and rural areas in various physical and biophysical properties leads to a local scale modification of an urban climate. Though the overall spatial extent of the urban area is minimal, its entanglement is attached to half of the world’s population living in the urban area [3]. The world’s total urban area is expanding twice its population growth [4]. Hence, the modifications in the local climate generated by urban areas are imperative to understand its impact on climate and human health.

The phenomenon of the Urban Heat Island (UHI) is one of the best examples of modifying an urban climate mainly caused due to the process of urbanization. It mainly indicates the elevated temperatures in the urbanized area compared to the surrounding rural area [5]. Sometimes depending upon the response of local physical and biophysical factors to the existing climatic conditions leads to negative thermal alteration in the rural area; this phenomenon is known as the Urban Cool Island (UCI) [6]. These UHI and UCI reverberations are consequences of the disturbed cycles of the absorption and redistribution of incoming solar radiation by altered urban and rural surfaces. In the past, several studies have revealed that UHI/UCI can affect human health [6], economic growth [7], emission of greenhouse gases [8], stormwater runoff [9], rate of mortality [10], modification of local climate [11], etc. Thus, it is essential to comprehend the role of various parameters in the formation of UHI to design mitigation strategies to control the contribution of those factors in the development of the UHI/UCI.

Several studies found that alterations in vegetation strength, albedo, and partition of surface energy balance, sky view factor are the major driving factors that regulate the magnitude of the UHI [12,13,14,15]. In addition, previous research has shown that these parameters mentioned above can act as proxy variables to explain the phenomenon of UHI/UCI [16]. Most parameters associated with an urban area, such as the sky view factor and vehicle density, remain constant during the pre-monsoon and winter. In contrast, parameters, such as soil moisture, vegetation strength, etc., mainly influence the rural microclimatic conditions and induce alterations in the magnitude of the UHI/UCI concerning the season. Thus, it is essential to model the behavior of the UHI/UCI and quantify the seasonal changes in the magnitude of UHI/UCI related to those seasonally altered variables. To date, various models have been developed and introduced to study the phenomenon of UHI.

There are mainly three types of models viz. Numerical, Physical, and Empirical models to scale out and quantify the seasonal behavior and changes in the UHI/UCI. Each has its advantages and disadvantages. As we know, the spatial extent of the cities is much smaller than the grid size of the global climatic models, which are used as input to the numerical models. Therefore, it is challenging and computationally expensive to model other associated parameters to UHI/UCI. Subsequently, these models are developing continuously; hence, analyzing each parameter and behavior of UHI at stationary conditions is challenging. In earlier studies, physical models are mainly used for dispersion analysis, wind flow pattern, surface roughness analysis, etc. [17]. An approach to empirical modeling depends upon the statistical algorithm. These models mainly deal with an observed dataset. The only limitation of an empirical model is that they are restricted to a particular location. In UHI studies, among all the empirical models, the statistical model is commonly used to model and analyze the relationship of UHI with other physical and meteorological parameters. Several researchers have used this method to quantify and model the impact of UHI [5].

In the case of India, cities are mainly surrounded by agricultural fields, due to which higher albedo, emissivity, and rate of evapotranspiration have been observed over the rural area compared to the nearby urban area. This type of vegetation gradients and evapotranspiration between urban and rural areas leads to lowered surface temperature in rural areas. Throughout this process of evapotranspiration, vegetation releases water vapor into the atmosphere, which contributes to reducing the air temperature. On the contrary, impervious surfaces cover urban areas, such as conventional roofs, roads, and sidewalks. As a result of urban expansion, more vegetation is removed and replaced by more impervious surfaces (i.e., buildings and paving), actuating the reduction in the rate of evapotranspiration that promotes the incremental trends of the temperature inside the urban areas. The vegetation gradient between the urban and rural areas, which decides the magnitude of the UHI, is sensitive to seasonal variation and crop cycle. During the pre-monsoon season, less soil moisture, inactive agricultural practices, and deficient irrigation make rural land temporarily barren, which leads to having hotter surfaces in the rural areas, while due to enough irrigated parklands and roadside vegetation inside the cities makes it cooler than the surrounding rural area which may enhance the magnitude of UCI. Compared to the conditions mentioned above of the pre-monsoon season, in the winter, due to enough precipitation and agricultural practices, it shows the ideal the UHI case where urban areas are warmer than surrounding rural areas. According to [18], nearly all cities in India act as a UCI during the pre-monsoon season, which promotes a higher mortality rate due to the intense heat wave events in the rural areas near the urban vicinity. To resist such events, we need to quantify and understand the role of vegetation gradient between the urban and rural areas on a different spatiotemporal scale to model the behavior of the UHI. Several studies [19,20] have demonstrated the response of UHI and UCI with the rurally dominant parameters during the pre-monsoon and winter seasons. However, little attention has been paid to quantifying the seasonal changes in UHI/UCI associated with the seasonally altered parameters.

The phenomenon of the UHI is mainly divided into three types, i.e., Surface Urban Heat Island, atmospheric urban heat island, and sub-Surface Urban Heat Island. In this study, we mainly focused on the first type of heat island, known as the Surface Urban Heat Island. We will now indicate the Surface Urban Heat Island and surface cool island as the SUHI and SUCI, respectively. The SUHI/SUCI mainly deals with the differences between the urban and rural Land Surface Temperature (LST). In the past, several researchers [21,22,23] used the LST data obtained from the MODIS satellite has been used to quantify the phenomenon of SUHI. For the western part of the Indian region, Pandya et al. [24] evaluated the quality of LST obtained from the MODIS [24]. He found that LST obtained from the MODIS satellite is well correlated with the ground observation with the bias ranging from 0.7–7.6 °C with the correlation index >0.9. The MODIS LST product has been validated using ground-based measurements and other satellite-based LST products, and the results have shown good agreement with these independent datasets. Therefore, with the help of LST data obtained from the MODIS, this study aims to (1) Analyze and model the seasonal variation of the SUHI/SUCI based on the climatology of SUHI/SUCI; (2) Quantify the seasonal alteration of the SUHI/SUCI concerning seasonally altered parameters.

2. Study Area

The present study was carried out over nine developing cities in India (Figure 1). The population of each city is greater than 0.4 million [25]. These cities mainly span the four states of India, i.e., Maharashtra, Karnataka, Gujarat, and Andhra Pradesh. The physical and land surface characteristics of selected cities are different from each other. At the same time, the local climatic conditions of the cities are also different due to the variation in their topography and geographical location. These cities mainly fall under three climatic zones, i.e., hot and dry, hot and humid, and composite climate [26]. Due to the strong demand in the residential and industrial sectors, major cities, such as Hyderabad, Ahmadabad, Pune, Nashik, and Aurangabad, are seeing rapid urban development growth.

On the other hand, cities, such as Kolhapur, Parbhani, Bellary, and Hubbali, are surrounded by agricultural land where the urbanization rate is low compared to the larger cities. This study has been carried out for the seasons of the pre-monsoon (March, April, and May, MAM) and winter (November, December, January, and February, NDJF). The pre-monsoon and winter seasons have been defined based on the report published by the Indian Meteorological Department (IMD) [27]. Due to cloud cover, the MODIS datasets obtained during the monsoon season (June, July, August, and September, JJAS) did not meet the required quality standards. As a result, we have excluded the monsoon season from our analysis. The Climatological mean pre-monsoon surface temperature of the cities ranges between 39.44 °C to 45.68 °C, while during the winter season, it spans between 30.20 °C to 33.42 °C. The detail information about various physical and meteorological parameters is given in

Table 1. Characteristics of selected nine cities of India.

Cities	Geographical Location	Population	Elevation (m)	Area (km2)	Mean Premonsoon Surface Temperature (°C) (2006–2016)	Mean Winter Surface Temperature (°C) (2006–2016)
Aurangabad	19.87° N 75.34° E	1,508,000	568	139	45.68	31.18
Hubbali	15.36° N 75.12° E	944,000	671	404	42.96	33.32
Hyderabad	17.38° N 78.48° E	6,810,000	505	650	39.44	30.2
Kolhapur	16.7° N 74.24° E	549,236	546	120	41.53	32.05
Nashik	19.99° N 73.70° E	1,486,053	700	268	42.48	31.41
Pune	18.52° N 73.85° E	3,120,000	560	424	42.26	32.12
Ahmedabad	23.02° N 72.57° E	5,307,000	53	464	41.99	30.21
Parbhani	19.26° N 76.77° E	410,445	347	37.8	43.15	32.42
Bellary	15.13° N 76.92° E	3,070,000	485	89.9	43.46	33.42

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3. Data and Methodology

3.1. Dataset Used

For this study, we analyzed data collected from MODIS-Terra, i.e., daytime eight-day composite data (MOD11A2) of Land Surface Temperature (LST) and monthly fused data (MOD13A3) of Normalized Difference Vegetation Index (NDVI). These datasets were obtained from the USGS website in a clear sky condition and further processed to maintain the images’ quality and equal temporal granularity. In addition, MODIS quality control flags were used to check the quality of the datasets. As a part of pre-processing, the original projection of the datasets, i.e., sinusoidal, is converted to the geographical projection. We know that satellite datasets have their qualms depending upon many other factors. In 2002, Trishchenko et al. [28] studied the impact of the sensor’s spectral response function on NDVI measurements to evaluate that it could result in their qualms between 0.02 to 0.06 in the absolute values for the NDVI estimated from the MODIS dataset. NDVI is mainly used to quantify vegetation vigor by measuring the difference between vegetation response within a near-infrared (NIR) and red (RED) channel of the electromagnetic radiation spectrum. It can be calculated by using the following equation:

$$\mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{RED}}{\mathrm{NIR}+\mathrm{RED}}$$

(1)

In this formula, NIR is the reflectance value in the near-infrared band, and Red is the reflectance value in the red band. The values of NDVI range from −1 to 1. The values closer to 1 show higher vegetation vigor, while the measurements closer to −1 represent lower vegetation strength. Several researchers [29,30] conducted their study to compare the MODIS LST with the in situ and the LST retrieved from the other satellites. Their analysis found a good confirmation index between LST obtained from the MODIS and other sources over India and other parts of the world. The secondary data of population, which act as a proxy to various other parameters, such as anthropogenic heat and built-up area, were obtained from the 2011 Census Report, Government of India and utilized. The details about all the datasets and their sources are given in

Table 2. Descriptions of the dataset used.

Parameter	Data	Temporal Resolution	Spatial Resolution	Period	References
Land Surface Temperature (Daytime)	MOD11A2	8 Day	1 km	2006–2016	https://lpdaac.usgs.gov/ (accessed on 11 September 2021).
NDVI	MOD13A3	Monthly	1 km	2006–2016	https://lpdaac.usgs.gov.modis (accessed on 11 September 2021).
Population	Census, 2011			2011	http://www.censusindia.gov.in (accessed on 12 August 2021).

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3.2. Methodology

LST (8 days) and NDVI (monthly) data were obtained from 2006 to 2016 from MOD11A2 and MOD13A3, respectively. The temporal resolution of both datasets were not equal. Thus, the eight days of LST data were converted to the monthly mean datasets. In the further part, the monthly mean LST and NDVI datasets were converted to the seasonal mean datasets for the pre-monsoon (March to May) and winter (November to February) seasons. The quality of the MODIS datasets during the monsoon (June to September) season was not up to the mark due to the cloud cover; hence, we have not integrated the monsoon season for our analysis. Afterward, all the seasonal data were calculated over the nine developing cities of India for the 11 years, averaged out, and mean Climatological data were prepared for the pre-monsoon and winter. During the entire data pre-processing and preparation procedure, null values in the datasets were excluded. Figure 2 represents a general flow of data processing and methodology flow.

3.2.1. Defining the Boundaries of the Urban and Rural Area

The UHI intensity mainly quantifies the difference in LST between urban and rural areas, so determining the urban and rural area boundary is essential. Urban area expansion is not restricted to municipal boundaries in cities, such as Ahmadabad and Pune. To mark these limits of urban–rural areas accurately, MODIS LULC data are used. Each city’s buffer distance from the urban boundaries towards the rural region varies. The buffer distance for the smaller cities lies between 4–6 km, while due to the large urban sprawl, a larger buffer distance, i.e., 7–8 km, was considered for further evaluation. During this analysis, the buffer width is kept constant at 1 km.

3.2.2. Estimation of Surface Urban Heat Island (SUHI)

The method of urban–rural buffer analysis has been adopted to investigate the intensity of the UHI. In this method, the difference in mean LST between the urban and rural areas has been considered to calculate the SUHI magnitude (Equation (1). This method considers the averaged LST of rural buffer areas along each direction of the urban areas as its significant advantage.

∆T = Tu − Tr ,

(2)

where,

• ∆T: Surface Urban Heat Island Intensity (SUHII).
• Tu: Mean Land Surface Temperature of the Urban Area.
• Tr: Mean Land Surface Temperature of the Rural Buffer Area.

With the equation mentioned above (2) and the averaged LST data obtained for 2006–2016, SUHII for the nine cities has been calculated for both seasons, i.e., pre-monsoon and winter.

3.2.3. Evaluation of Urban-Rural Vegetation Gradient

A recent study documented that the amount of vegetation inside the city or around the rural area influences the magnitude of the SUHI [31]. However, the association between vegetation gradient and the UHI modification is still unexplored. Thus, it is essential to quantify this impact of the vegetation gradient on the SUHI. On a seasonal scale, the vegetation gradient between urban and rural areas alters due to meteorological changes affecting the cities’ SUHII. Therefore, at a seasonal scale, it is necessary to elucidate the relationship between vegetation gradient and SUHII. During this study, the vegetation gradient was calculated with the help of averaged values of NDVI data obtained for 2006–2016. In this process, the urban–rural buffer analysis method was adopted to estimate the vegetation gradient over each city.

∆NDVI = NDVIu − NDVIr,

(3)

where,

• ∆NDVI: vegetation gradient.
• Tu: Mean NDVI of the Urban Area.
• Tr: Mean NDVI of the Rural Buffer Area.

4. Results and Discussion

4.1. Seasonal Variation of SUHI

For the pre-monsoon and winter seasons, with the aid of 11 years of Terra MODIS LST datasets, the extensiveness of SUHI over the nine developing cities of India was calculated (Figure 3). The analysis shows that, during the pre-monsoon season, the maximum intensity of SUHI (1.47 °C) is observed over Kolhapur, while the minimum magnitude of SUHI (−0.85 °C) is observed over the city of Nashik. In the pre-monsoon season, out of the nine cities, four, viz. Parbhani, Hyderabad, Nashik, and Bellary, represent the Urban Cool Island (SUCI). This SUCI phenomenon mainly arises due to reduced vegetation and rising temperature in the adjacent rural part of the urban areas. Still, in the case of Hyderabad, no higher magnitude has been found in the vegetation of the urban area in both seasons. This phenomenon of the SUCI during the pre-monsoon and winter in Hyderabad may be the effect of a large water body in the middle of the town.

In the winter, the maximum intensity of the SUHI is found over Pune City (0.95 °C), followed by Ahmadabad (0.61 °C). Some cities, such as Aurangabad, Hubbali, Kolhapur, Hyderabad, and Bellary, show the negative behavior of the SUHI, i.e., the SUCI, during the winter season.

Figure 4 depicts that in the winter season, in cities experiencing the phenomenon of the SUCI, a negligible difference (−0.003 to −0.009) has been observed in ∆NDVI. On the other hand, cities with a higher magnitude of ∆NDVI represent the typical intensified profile of the UHI during the winter season. These seasonal alterations in the SUHI intensity may have occurred due to the seasonal changes in NDVI.

4.2. Statistical-Based Modelling of Premonsoon and Winter SUHI

In the further part of the study, a statistical analysis was carried out to explore the association of the SUHI with other vegetation and climatic parameters on a different spatial and temporal scale, which may elucidate the spatial and seasonal variations in the SUHI. To model the SUHI regression-based statistical model is developed for the pre-monsoon and winter seasons. A simple linear regression was performed between the SUHI and the other variables to identify the suitable forecaster. The other parameters were selected based on former literature and their physical significance to the phenomenon of the SUHI. To establish the SUHI model for the pre-monsoon and winter seasons, we mainly considered the five parameters, i.e., the mean land surface temperature of the urban and rural area, population, the vigor of vegetation in the rural part, and ∆NDVI. As we know, the total amount of incoming solar radiation is approximately equal for adjacent rural and urban areas. Still, the different land surface properties of the urban–rural area led to a difference in the magnitude of surface temperatures, which gave rise to the SUHI.

Thus, during this study, the LST of the urban and rural areas has been taken as a proxy variable to the land surface characteristics of the urban and rural areas, such as albedo, emissivity, heat capacity, etc. Various studies have been conducted, such as [32,33], to explain the negative relationship between temperature and vegetation. The vegetation in a particular territory keeps that area cool through evapotranspiration. Hence, the total amount of vegetation present in the urban and rural areas controls the temperatures of the respective area. It is considered one of the significant parameters that can pedal the magnitude of the SUHI. In addition, the disparity of vegetation strength between urban and rural areas can alter the natural cooling process of the environment, resulting in changes in the temperature difference between urban and surrounding rural areas. Besides these climatic and biophysical parameters, the population of the urban area is a significant factor that plays an essential role in the formation of SUHI. The more population in the urban area represents the high density of buildings, vehicles, roads, low sky view factor, and many industries. It creates more anthropogenic heat that gets discharged and trapped in the urban environment, making the urban area warmer. Hence, the population is also essential for modeling the SUHI effect. Figure 5 represents the correlation coefficient, i.e., R, between SUHI, and other predictors for the pre-monsoon and winter seasons.

During the pre-monsoon season, the significant relationship between ∆NDVI and the SUHI confirms that the difference in the vegetation vigor between urban and rural areas is one of the major factors responsible for developing the phenomenon of the SUHI in the pre-monsoon season. In addition, the association between population and surface temperature of the urban area reveals that an increase in the population of the urban area makes the city warmer. Compared to the pre-monsoon season, the association between the population and Tu becomes stronger during the winter, while the SUHI has the strongest association with Tr. It characterizes Tr as a controller of the SUHI effect for the winter season. Using the above analysis, we constructed the regression-based model for the SUHI during winter and pre-monsoon. We have mainly used three predictors to develop a model for the pre-monsoon season. Tu, population, and ∆NDVI. Likewise, the correlation analysis and the multivariate regression model (Equation (4)) also denotes the dominance of ∆NDVI during the pre-monsoon season. This regression model has a coefficient of determination (R²) of 0.97 with p < 0.0001 and the Root Mean Square Error (RMSE) of 0.28 °C. The ∆NDVI as an alone predictor coefficient of determination (R²) was 0.65; other variables were added to improve the model’s predictability.

SUHI in pre-monsoon = 43.08 − 0.135 (Tu) − 20.72 (∆NDVI) − 0.00000025 (Population)

(4)

During the winter, vegetation coverage in the surrounding rural area is a decisive factor controlling the magnitude of the SUHI. Initially, NDVIr as an alone predictor coefficient of determination (R²) was 0.62. Thus, to perk up the model performance, additional predictors were added.

SUHI in winter = 107.96 − 0.37 (Tr) + 11.08 (NDVIr)

(5)

The model of the SUHI for a winter season has a coefficient of determination (R²) of 0.89 with p < 0.0001 and the RMSE of 0.24 °C.

4.3. The Relationship between ∆Seasonal SUHI and ∆Seasonal Change in Vegetation

The ∆Seasonal SUHI is obtained by calculating the difference between the mean of the pre-monsoon and wintertime SUHI. At the same time, the ∆Seasonal change in vegetation is retrieved by calculating the disparity of ∆NDVI between both seasons. Figure 6 elucidates the relationship between alteration in the seasonal NDVI and the difference between the seasonal SUHI. The negative affiliation with the correlation coefficient R = −0.646 (p < 0.001) at a 95% confidence level is found between seasonal change in Sthe UHI and seasonal variation in the difference between urban and rural vegetation. After observing the association between the ∆Seasonal SUHI and ∆Seasonal change in vegetation, it is required to find the significant controlling factors which decide the seasonal alterations in SUHI.

Recently, a few studies noted that agricultural activities in rural areas tend to have more seasonal variation in the land surface properties than in the urban area [20,34]. It suggests that the magnitude of perturbations in LST due to land surface properties is greater over the rural area. Some studies explained that the population data as a proxy for the built-up area, anthropogenic heat, etc. [35]. Thus, to model the ∆Seasonal SUHI, we have used a multivariate regression method and considered three parameters viz. ∆Seasonal change in vegetation, ∆Seasonal rural LST, and population, which quantifies the seasonal change in SUHI, where

∆Seasonal SUHI = SUHI winter − SUHI premonsoon

(6)

∆Seasonal change in vegetation = ∆NDVI winter − ∆NDVI premonsoon

(7)

∆Seasonal rural LST = LST of Rural Site (winter) − LST of Rural Site (premonsoon)

(8)

The equation, “∆Seasonal SUHI = −8.353 + (0.0142) × ∆Seasonal rural LST—(17.40) × ∆Seasonal change in vegetation”, explains the changes in the seasonal SUHI with the coefficient of determination R² = 0.68 where the p-value < 0.005 for all the attributes of the equations. It indicates that a decrement in a ∆Seasonal change in vegetation by 0.1 leads to an increase in the ∆Seasonal SUHI by 1.74 °C, which is quite a significant augmentation if other variables remain constant. Hence, to reduce the intensity of the SUHI at a seasonal scale, it is necessary to reduce the magnitude of ∆Seasonal change in vegetation. The advantage of the above equation is that it can explain the ∆Seasonal SUHI variations of up to 68% over the nine cities in India using the readily available satellite data of NDVI and LST. We consider this equation true, at least for cities under study.

5. Conclusions

This study was mainly divided into two parts, i.e., (1) Assessment of the seasonal variation of SUHI and (2) the response of seasonal alterations in the SUHI to the seasonal variations in the vegetation strength. This study was performed over nine developing cities in India using satellite data for 2006–2016. These nine cities were chosen carefully based on their differences in (a) climatic zones, (b) geographical location, and (c) topographical characteristics.

In this study, during the pre-monsoon (March to May) and winter (November to February) seasons mean intensity of the SUHI and the ∆NDVI were derived for each city. A result shows that the SUHI’s magnitude mainly depends upon the ∆NDVI. The positive values of ∆NDVI represent the phenomenon of the Urban Cool Island, where the city of Hyderabad was an exceptional case. In both the pre-monsoon and winter seasons, Hyderabad experiences the SUCI, which may be the effect of a large water body located in the middle of the city. It needs to be analyzed further to validate and quantify the impact of the water body on the SUHI.

Further statistical analysis between the SUHI and other studied variables shows that during the pre-monsoon season, ∆NDVI is the dominant factor which explains 97% of the SUHI with the RMSE of 0.28 °C, while in the winter season temperature of the rural region is the most significant parameter, which describes the 89% of the SUHI with the RMSE of 0.24°. The performed correlation and regression analysis confirms the sensitivity of each parameter to the mean seasonal SUHI so that one can concentrate on specific variables while designing the mitigation strategies for the SUHI. In the second part of the study, we quantified the response of the SUHI to the seasonal changes in vegetation strength. During this analysis, we found that an alteration in the ∆Seasonal change in vegetation explains 68% of the variation in the ∆Seasonal SUHI. As we know, the variation in vegetation strength during the winter and pre-monsoon seasons mainly depend upon the monsoon disparity. Thus, further exploration of seasonal changes in the magnitude of the SUHI and its association with the monsoon variation through its impact on seasonal vegetation strength needs to be evaluated.