Jaipur, the capital city of the north western Indian state of Rajasthan, is surrounded by fertile plains, hills, and desert areas. The Aravalli Mountains enclose the city from three sides and serve as natural protection for the city. The city is at an average elevation of 432 meters above the mean sea level and has a semi-arid climate with hot summers and mild winters as characteristic features. With a population of approximately 2.8 million ( Census of India, 2011 ), the city is an intrinsic example of proper urban planning and development ( Figure 1 ).

Ahmedabad, an economic and financial hub in the western state of Gujarat, is known for its varied geographical features. The city is located on the banks of the Sabarmati River, with the landscape composed of flat fields and undulating terrain with an average elevation of 50 meters above the mean sea level. Ahmedabad has a semi-arid climate characterised by hot summers and mild winters. Arid landscapes characterise the city’s surroundings, and industrial areas and agricultural zones dominate its suburbs. With an approximate population of 5.5 million ( Census of India, 2011 ), Ahmedabad is famous for historical events, economic dynamism, and urban development ( Figure 1 ).

Jaipur and Ahmedabad, located in the western part of India, are experiencing hot summers and mild winters. The rugged Aravali hills surround Jaipur, while Ahmedabad is situated along the banks of the Sabarmati River. Ahmedabad’s average summer maximum is 39˚C, and average minimum is 24˚C while in winter, the average maximum temperature is 30˚C, and the average minimum is 15˚C. For Jaipur, in summer, the average maximum temperature is 40.1˚C, and the average minimum is 25.3˚C whereas in winter, the average maximum temperature is 22.6˚C, and the average minimum goes down to 7.9˚C. Ahmedabad is called the “Manchester of India” due to its thriving textile industry, whereas Jaipur also encompasses industries like Cotton textile, leather, mineral-based, wooden, etc. Both the cities are enriched with cultural heritage and historical significance.

The current study’s data sources primarily involved using geospatial data in the form of satellite images. The satellite images of the Landsat 8 OLI, TIRS, and MODIS Terra Sensors were acquired from the USGS Earth Explorer and NASA Earth Data platforms. The details of the Data acquired are given in Table 1 .

Source: Open-source satellite data.

After the acquisition of the satellite data, GIS-based tools and techniques were incorporated to analyse and interpret the datasets. The MODIS Terra data was used for the calculation of the Land Surface temperatures of the cities, whereas the Landsat 8 Data were utilised for the mapping and identification of Land Surface Characteristics ( Zhu & Woodcock, 2014 ; Huang et al., 2009 ; Hassan et al., 2016 ; Tao et al., 2018 ; Spruce et al., 2013 ; Park et al., 2019 ). For this purpose, the following method was used to determine the relationship between LST and several geospatial indices to assess the green urban environment. The UTFVI plays a prominent role in ecologically evaluating the Urban Environment by considering the Land Surface Temperatures (LST). The NDBI helps indicate the high built-up area, NDVI, which is the numerical indicator for evaluating the health and density of vegetation. MNDWI calculates the water concentration in vegetation canopies, and NDBaI is beneficial in identifying and mapping the areas of bare soil or exposed ground. The following indices were incorporated in the study:

The UTFVI plays a prominent role in ecologically evaluating the Urban Environment by considering the Land Surface Temperatures (LST). It considers the impact of the LST of different locations with the overall mean LST of the Urban Area. Using the Land Surface Temperatures, the UTFVI index was used to Surface Urban Heat Island (SUHI) ( Sobrino & Irakulis, 2020 ; Toy et al., 2007 ; Liu & Zhang, 2011 ). The following equation calculates it:

$\text{UTFVI}=\text{1}-\left({\text{LST}}_{\text{Mean}}/{\text{LST}}_{\text{Pixel}}\right)$(1)

LST is the land surface temperature obtained by the satellite data using MODIS Terra. LST_Pixel is the Pixel value of the LST of different locations, and LST_Mean is the Mean value of the whole urban area considered.

After the computation of the above indices, Area Identification and Extraction were performed to analyse Land Surface Characteristics and Urban Heat Exposure using GIS tools and Statistical techniques.

The NDVI incorporates the Red and Infrared bands of the satellite data products to delineate the Vegetation cover at a particular location from its surrounding land uses ( Reed et al., 1994 ; Huang et al., 2019 ; Piao et al., 2011 ; Mao et al., 2012 ). The following Equation calculates it:

$\text{NDVI}=\frac{\text{Near Infrared}-\text{Red}}{\text{Near Infrared}+\text{Red}}$

$\text{NDVI for Landsat 8}=\frac{\text{Band}5-\text{Band}4}{\text{Band}5+\text{Band}4}$(2)

The NDBI utilises the middle and near-infrared ranges to differentiate the built-up areas from other features. The following equation calculates it ( Zha et al., 2003 ):

$\text{NDBI}=\frac{\text{Short wave Infrared}-\text{Near InfraRed}}{\text{Short wave Infrared}+\text{Near InfraRed}}$

$\text{NDBI for Landsat 8}=\frac{\text{Band}6-\text{Band}5}{\text{Band}6+\text{Band}5}$(3)

An improved and modified approach ( He & Reith, 2023 ) gives better results for extracting the Built-Up Land and is calculated by the following equation:

$\text{Modified NDBI}=\text{NDBI}-\text{NDVI}$(4)

The NDBaI utilises the Middle-range Infrared and Thermal range bands to extract the Bare Land from the surrounding areas ( Zhou et al., 2014 ). The following equation calculates it:

$\text{NDBaI}=\frac{\text{Short wave Infrared}-\text{Thermal}}{\text{Short wave Infrared}+\text{Thermal}}$

$\text{NDBaI for Landsat 8}=\frac{\text{Band}6-\text{Band}10}{\text{Band}6+\text{Band}10}$(5)

The MNDWI proves to be more effective in improving and extracting water-related information from within the nearby built-up environment or other land uses as it can minimise or eliminate noise from built-up land, a feature that sets it apart from the NDWI ( Xu, 2006 ). The following equation calculates it:

$\text{MNDWI}=\frac{\text{Green}-\text{Short wave Infrared}}{\text{Green}+\text{Short wave Infrared}}$

$\text{MNDWI for Landsat 8}=\frac{\text{Band}3-\text{Band}6}{\text{Band}3+\text{Band}6}$(6)

The Thermal band data acquired by the satellite sensors is generally utilised to study the Land Surface temperatures. The Brightness values measured are converted into the Temperature values using the following equation in GIS Software:

Temperature in ˚C = DN * 0.02 − 273.15

where DN stands for the Digital Number of the MODIS Land Surface Temperature Satellite Data Product.

The calculated results of LST ( Figure 2 ) reveal that in Ahmedabad, the maximum recorded LST stands at 45.19˚C, while the minimum is 37.31˚C, resulting in a temperature range of 7.88˚C. Similarly, Jaipur exhibits slightly higher temperatures, with a maximum LST of 45.51˚C and a minimum of 37.17˚C, resulting in a slightly wider temperature range of 8.34˚C ( Figure 3 ).

The UTFVI was used to analyse the spatial patterns of the SUHI in both cities. When the UTFVI was calculated, the values were reclassified into high, medium, and low UHI potential zones ( Figure 4 ).

In Ahmedabad, the analysis reveals that 35.53 per cent of the total area is classified as having a low potential for UHI formation, while 50.92 per cent falls within the moderate potential category, and 13.55 per cent is designated as a high potential UHI zone. Similarly, in Jaipur, 30.45 per cent of the city’s total area is identified as a low potential UHI zone, with 56.86 per cent categorized as moderate potential and 12.69 per cent classified as a high potential UHI zone ( Figure 5 ).

In Ahmedabad, areas characterized by high potential UHI include industrial zones along the city’s eastern parts, the Old City area, Khadia, and certain peri-urban regions primarily consisting of barren land towards the southern outskirts. Conversely, locations with low potential UHI zones are generally found along the Sabarmati River, peri-urban regions, and surrounding waterbodies such as Kankaria Lake and Chandola Lake. On the other hand, in Jaipur, high-potential UHI zones are predominantly found in industrial areas in the northwest part of the city and specific localities like Shastrinagar and Bapu Bazar. On the other hand, areas with low potential for UHI development in Jaipur include regions characterized by dense vegetation, such as those surrounding Jal Mahal, Jhalana Safari Park, the Aravali Mountains, and Central Park ( Table 2 ).

Source: Open-source satellite data.

The values obtained through Normalised Difference Vegetation Index (NDVI) Mapping ( Figure 6 ) reveal the information about the vegetation cover in Ahmedabad and Jaipur, ranging from a maximum of 0.560 to a minimum of −0.072 in Ahmedabad and from a maximum of 0.550 to a minimum of −0.069 in Jaipur. Both cities display certain areas with dense vegetation, with Ahmedabad showing slightly higher NDVI values than Jaipur ( Table 3 ).

Source: Open-source satellite data.

The area statistics reveal that approximately 26.77 per cent of Ahmedabad’s total area is covered by high vegetation. This vegetation is predominantly found along the Sabarmati River, spanning the city’s northern and southern parts. This greenery contributes to biodiversity and environmental health by improving air quality and mitigating the urban heat island effect. On the other hand, Jaipur’s healthy vegetation covers a slightly more significant portion, accounting for 25.39 per cent of the total area. In Jaipur, this vegetation is concentrated around the Aravali Mountains, a significant geographical feature of the region. Additionally, dense vegetation is found in and around the areas like Jal Mahal, Jhalana Safari Park, and Central Park ( Table 7 ).

Upon mapping the areas of Built-up areas through MBI, the values highlight the extent of urbanization in Ahmedabad and Jaipur ( Figure 7 ). The values range from a maximum of 0.318 to a minimum of −0.930 in Ahmedabad, and from a maximum of 0.291 to a minimum of −0.932 in Jaipur; both cities exhibit considerable built-up areas ( Table 4 ). This suggests that urban development and infrastructure expansion have significantly altered the land cover in both cities, leading to increased impervious surfaces and reduced vegetated areas.

Source: Open-source satellite data.

Most of Ahmedabad’s urban landscape is covered by built-up areas, accounting for 56.14 per cent of the total geographical area. These areas include densely populated neighbourhoods such as Dariyapur, Saraspur, Kalupur, the Old City, Khadia, and Asarwa. Meanwhile, in Jaipur, high-built areas cover about 50.83 per cent of Jaipur’s total geographical area. These areas include Shastrinagar, Bapu Bazar, Laxminarayan Puri, Subhash Chowk, and Sundarvihar ( Table 7 ).

The values obtained by the MNDWI mapping indicate the spatial cover of water bodies in both cities ( Figure 8 ). The values range from a maximum of 0.347 to a minimum of −0.504 in Ahmedabad and from a maximum of 0.139 to a minimum of −0.516 in Jaipur; both cities display the presence of water bodies ( Table 5 ).

Source: Open-source satellite data.

Water bodies cover 4.64 per cent of Ahmedabad’s total area, including the Sabarmati River, Kankaria Lake, Chandola Lake, Malek Saban Lake, Vanar Vat Talab, Piranha Lake, and Isanpur Lake. Jaipur has a smaller proportion of water bodies, covering only 1.21 per cent of Jaipur’s total area, including significant water bodies like Jal Mahal and Amanishah Nala ( Table 7 ).

The Normalized Difference Bareness Index (NDBaI) values detail the extent of bare land cover in Ahmedabad and Jaipur ( Figure 9 ). The values range from a maximum of 0.252 to a minimum of −0.556 in Ahmedabad and from a maximum of 0.384 to a minimum of −0.069 in Jaipur, indicating varying degrees of bareness ( Table 6 ).

Source: Open-Source Satellite data.

Bare land areas cover 11.30 per cent of Ahmedabad’s total area, primarily in peri-urban regions surrounding the city. These areas may include undeveloped or agricultural land and patches of barren terrain. In contrast, as compared to Ahmedabad, Jaipur constitutes high bare land areas with approximately 20.34 per cent of Jaipur’s total area. These areas are primarily situated in peri-urban regions in the western and southern parts of the city and around Mansarovar ( Table 7 ).

Source: Open-Source Satellite data.

The relationship between Land Surface Temperature (LST) and land surface characteristics in the cities is crucial for understanding urban heat dynamics. Both cities exhibit significant variations in land surface characteristics, with Ahmedabad boasting 26.77 per cent higher vegetation cover than Jaipur, which is slightly lower at 25.39 per cent. Areas with abundant Vegetation, such as along the Sabarmati River in Ahmedabad and around the Aravali Mountains in Jaipur, possess lower LSTs due to the cooling effects of Vegetation through transpiration and shading. Conversely, built-up areas, which constitute the majority of urban landscapes in both cities, i.e. 56.14 per cent in Ahmedabad and 50.83 per cent in Jaipur, exhibit higher LSTs. In contrast, in Ahmedabad, the high LSTs were visible in the areas of the older city, which lack the proper planning strategies, but on the other hand, the significant UHI was visible in the north western part of the city, which is primarily owned by industrial activities. Jaipur is a set example of a city that has been planned since it was established, which has ensured the green cover in the city and controlled the haphazard development. Thus, in Jaipur, only certain city pockets witness high LST, which generally include the densely populated areas with high building density. The presence of water bodies moderates surface temperatures by absorbing and storing heat, resulting in lower LSTs, such as in the regions along the Sabarmati River in Ahmedabad and regions along Jal Mahal in Jaipur, which exhibit the minimum values of LST. In contrast, regions with significant proportions of bare land, such as peri-urban areas surrounding both cities, experience higher LSTs due to increased solar radiation absorption and reduced evaporative cooling.

The present study reveals significant information about the Land Surface characteristics and their effect on the LST and microclimatic variations of the cities. The results from the open-source satellite images show that both the cities have temperature ranges of around 8 degrees Celsius. Both cities experience the UHI effect, but the difference is found in the locations of SUHI, where Jaipur has a majority of high LSTs around industrial areas, whereas Ahmedabad experiences high LST values in the city’s densely built-up areas. The healthy Vegetation and presence of water bodies in both cities modify the microclimate of the cities by mitigating the UHI phenomenon and providing greater thermal comfort to the residents.

The geographical distribution of elevated land surface temperature (LST) indicates that concrete zones are becoming urban heat island types. However, any urban zone’s LST can be lowered by having a lot of greenery and water features. In urban and peri urban settings, protecting green spaces and bodies of water is one of the most crucial responsibilities of planners and legislators. Cities can improve their urban thermal environment with the use of urban plans, and it is recommended that administrative authorities create a plan specific to each circumstance.