Urban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability.

  • Study examines how UHI gets affected by Landuse variation and Water cooling with remote sensing and geospatial analysis.

  • The findings inform urban planning and climate change adaptation in similar cities.

  • Urban water bodies can act as natural climate buffers by cooling.

  • The study illuminates the UHI effects on urban climate resistance.

Urbanization has a lot of adverse effects on the environment, mostly because of pollution, changes in the physical and chemical qualities of the air, and changes in the way the land is used (Son et al. 2017). The result of all these effects is an urban heat island (UHI), which is the rise in temperature of any manufactured area. This makes a ‘warm island’ that stands out from the ‘cool sea’ of the nearby natural landscape, which has a lower temperature. UHI describes the temperature disparity between urban areas and the surrounding countryside. Cities may have temperatures that are 12 °C warmer than the surrounding countryside. UHI causes urban areas to become ‘heat islands,’ as the acronym suggests. Closed isotherms indicating a significantly heated surface area constitute UHI. This is most often seen in places where people live, like towns and cities (Siddiqui et al. 2021).

In the books, there are three distinct kinds of UHI. Surface heat islands measure the infrared energy that surfaces give off and reflect; it is possible to find out where in a city the surfaces are the warmest (Shengyue et al. 2014). The canopy layer is the layer of air between the ground and the tops of trees or the roofs of buildings. This is where most people live, work, and play. The border layer is the layer of air above the canopy layer. Canopy and outer layer heat islands have to do with the temperature of the air. Over the past 10 years, studies and tests have been done in many places around the world to try to figure out why this is happening. There are several things that can cause a UHI (Wu et al. 2013). The main reason it gets warmer at night is because the heat energy that was taken during the day is still in the concrete, roads, and buildings. The energy is then slowly given off as long-wave radiation during the night (Li et al. 2018).

The thermal bulk qualities (like heat retention and heat conductance) and surface irradiative characteristics (like reflectivity and emissivity) of urban building materials like concrete and roads are different from those of rural building materials (Pongracz et al. 2006). This changes the energy balance of the city, which often makes it hotter than the rural areas around it. Albedo is the measure of how much light a material reflects to how much light shines on it (Bellum et al. 2022; Choudhary et al. 2022). High albedo surfaces include natural ground and forest, which are light and dry and reflect sunshine, so are cooler on the surface (Sharma et al. 2020c). The heat island effect is mostly caused by the low total reflectivity of the urban fabric and the lack of vegetation. When vegetation is not there, they cannot provide shade or evaporate water, which are two important ways to keep cool (Agam et al. 2007). This makes it easier for urban heat islands to grow. The shade cools the air by blocking sun rays from surfaces with low reflectance (reflectivity). Towns with more vegetation have better air quality and can manage rainwater better (Kumar et al. 2023a, 2023b).

Effects of shape or urban topology, which is about the shape, direction, and distance between buildings in a city, also play a part in how urban heat islands form. The shape of a city affects how the wind moves through it, how much energy it can absorb, and how well its surface can send long-wave radiation back into space. The tall buildings in urban areas have many surfaces that reflect light, causing sunlight to be absorbed. This makes it easier for the sun to heat urban areas. The term for this is ‘urban canyon effect.’ Also, the shape of a city can affect how many cars drive through it, which can lead to more heat and air pollution from cars (Lemonsu et al. 2015).

In the next couple of decades, the number of people living in cities is likely to grow by three to four times. UHI is something that has been seen to happen because of cities growing and getting more crowded. This happens when there are more buildings and roads and less green space. This makes the weather in towns warmer than the temperatures in the surrounding area. Observed temperature rises in towns have been between 1 and 3 °C during the day and up to 12 °C during the night (Zhou et al. 2016). Such changes can also have big effects on things like energy use, health, and the economy, and become a national problem. Most of this trend can be seen in the big cities, which are quickly becoming full. It can also be seen in the second- and third-tier cities, which are following suit quickly. UHI is something that can be seen in many places around the world. Many studies in the past have looked at UHI in a single metropolitan area for the Twin Cities Metropolitan Area (Zhong et al. 2015; Jain et al. 2022). Most studies on UHI have been done in places with wintry weather. Some tropical towns have also been investigated for the UHI effect. However there is no information about how UHI affects cities with a hot-semiarid climate, and only a small amount of study has been done on UHI in Indian cities for Bengaluru and for the National Capital Region.

Land is becoming scarcer due to the demands of both agricultural and population expansion. Altering how land is used has been a major factor in human-caused environmental change from prehistoric times. Rapid urbanization is transforming rural regions at a pace unseen in modern human history. The normal functioning of ecosystems is being drastically altered as a result (Chen et al. 2020). Understanding how much changes in urban land use and land cover contribute to the UHI effect is receiving a lot more focus as scientists realize the magnitude of the influence that humans have on urban ecosystems (Hulley 2012). Temperatures in urban areas are affected by the kinds of land utilized, with industrial and commercial areas having the highest daily surface temperatures, followed by airports, residential areas, and parks (de Oliveira Souto & Cohen 2021).

The study of UHI patterns with LST and land use variation in Pune has been conducted using satellite imagery and aerial photographs. Through land use classification, diverse groups were found, such as residential, commercial, industry, greenery, and open areas, and their effects on UHI have been looked at. The study also looked at how lakes, rivers, ponds, and other sources of water in the city cool it down. Temperature gauges were put in and around these bodies of water in a planned way to measure the surface and air temperatures. Also, things like the size, level, and quality of the water in the bodies of water, as well as the vegetation covering around them, were looked at to see affected temperature changes in the area. By looking at the UHI in Pune, this study hopes to help urban planners and lawmakers learn important things. The results can be used to produce ways to reduce the effect of UHI, like boosting green areas, making the best use of land, and putting water bodies into urban planning. In the end, these steps can help make Pune's urban environment more safe, adaptable, and livability. UHI effect needs to be considered as part of the general plan for towns to grow in a way that uses less carbon. It also helps us understand distinct factors that help reduce the UHI effect, such as the cooling effect of water bodies in urban areas.

Urbanization causes ‘Urban climate’ conditions. Urban and rural climates differ in temperature, humidity, wind speed and direction, and precipitation. Urban air may be 2 °C hotter and 10 times dirtier. Human-caused heat, wetness, and pollution exacerbate the temperature differential between urban and ‘undisturbed’ climates. Thus, human settlements' local climates affect urban climate and urbanization. High-density cities with higher temperatures than their suburbs are heat islands. The Climate of London contrasted urban heating with rural England temperature readings over 9 years (Mavrogianni et al. 2011). In the summers, roofs and pavement may reach 50–90 °C (Bohnenstengel et al. 2011). Cloudless skies and moderate breezes make cities 10 °C warmer than rural places (Farid et al. 2022). Buildings, roads, and concrete retain more heat than greenery, which is more abundant in the countryside. These surfaces affect the city's energy balance and each city's UHI is different.

UHI has several causes like humidity, wind, temperature, precipitation, and sunshine affect weather (Zhou et al. 2019). The weather circumstances might lead to higher phases of smog production by reduced wind speeds. Aspect ratio, wind direction, building wall thickness, apertures, and surface clutter impact surface heating and cooling. Large cities enhance UHI getting help from local high-rise buildings and narrow alleys, which may produce urban hot zones (Elmarakby et al. 2022). Ozone and carbon monoxide from gasoline and refrigeration pollute cities and impede solar energy, which increases ambient temperature. Atmospheric urban heat islands are weak throughout the day while critical after nightfall because urban infrastructure releases heat slowly.

Landuse & Landcover (LULC) changes impact UHI intensities and surface temperature. Urban water bodies alter surface temperature. Vegetation mitigates urban heat and perspiration cools dense vegetation. Stone, concrete, roads, and dark materials retain heat. These materials capture solar energy during the day and progressively release it in the urban fabric at night. Temperature and humidity affect long-wave radiation balance. Human heat influences the UHI in winter due to excessive energy demand and weak short-wave radiation (Li et al. 2011). Inappropriate zones of industrial and commercial heat the urban areas. Heat emanates from densely populated areas and commercial spaces. Road surfaces and most building materials do not filter or absorb storm water, therefore changing its natural path. In cities, 15% of precipitation infiltrates the soil and 55% runs off, whereas in nature, 50% infiltrates and 10% flows into watercourses (Li et al. 2014; Singh et al. 2023).

LULC patterns are shaped by natural, socio-economic, and human factors. UHI study in India's NCR discovered that urbanization had influenced minimum night temperatures. After 1986, Palam's nightly temperature was greater than Safdargunj's owing to the establishment of Asia's biggest residential colony in Dwarka and IGI airport's new international terminal. Nighttime temperatures and UHI effects grow due to decreasing green space, building density, evapotranspiration, and pollution (Buo et al. 2021). Land is limited due to population and agriculture (Kikon et al. 2016). Weather, floods, fire, temperature, and biological processes can alter land cover. Thus, LULC and optimal use information is essential for choosing, planning, and executing land use plans to meet criteria for basic human needs and welfare.

Complex material characteristics, sky view, solar intensities, wind velocity, and other variables impacted land surface temperature (LST) cooling and heating at various sites. Climate, hydrological, agricultural, and change detection models need LST (Sharma et al. 2021). Due to differential cooling and heating, barren terrain, and built-up land have enormous temperature fluctuations, whereas vegetation cover and water bodies have small ranges (Sharma et al. 2020a). Urban growth's effect on UHI has been assessed using temporal and geographical approaches such as Sharma et al. (2020c) LST difference between periods of a same site or rural–urban temperature difference (Li et al. 2017). Delhi's north-south and west-east temperature gradient suggests a minor UHI. LST distribution and change were analyzed using Landsat Thematic Mapper (TM) satellite images from 16 January, 5 March, 8 May, and 29 September 2011. January had the lowest average surface temperature and May had the highest. All LULC groups experienced warmer springs. Lakes, vegetation, and agriculture have the lowest winter temperatures, whereas urban–rural built-up regions, Yamuna River, and river sand have the highest (Pramanik & Punia 2020). All LULC groups showed cooler autumn temperatures than summers (Mallick et al. 2013).

LANDSAT images from October 1998 (TM) and 2002 (ETM+) have been used to determine LULC, land surface emissivity (LSE), normalized difference vegetation index (NDVI), and LST in Iran. The maximum likelihood classifier built LULC map included water, vegetation, rangeland, marsh, sand dune, woodland, and bare soil. Satellite images determine NDVI using red (0.63–0.69 μm) and near-infrared (NIR) (0.76–0.90 μm) bands. Sand dunes were the warmest and water bodies the coldest in Iran's South Karkheh sub-basin. LST and NDVI are linked negatively except for aquatic bodies. Wetlands exhibited the highest negative correlation (−0.7673), followed by vegetation (−0.6606) and sand dunes (−0.6440). Water bodies exhibited a (−0.4597) negative correlation, whereas forest, bare soil, and rangeland had low correlation values (−0.5540, −0.4794, and −0.4147, respectively).

A China-wide UHI and LULC investigation found large temperature anomalies in built-up terrain, strongly populated regions, and heavily industrialized districts. Landsat TM/ETM + , NDVI, and normalized difference built-up index (NDBI) for UHI investigation of Guangzhou, South China. Normalized difference bareness index (NDBaI) advised removing barren terrain from satellite images for poor vegetation in Guangzhou, China, produced the UHI effect. Land cover and LST maps from Landsat TM/ETM + showed that when NDVI is limited, NDVI, NDWI, NDBaI, and LST are negative, whereas NDBI and temperature are positive (Ju et al. 2013).

Urban water body research is limited compared to built-up, green landscape, and land usage. The literature has few research on UHI studies of diverse urban climates in India. Most research has been done in developed nations and planned cities, where circumstances are different from underdeveloped countries. UHI research findings are difficult to generalize to other cities. LULC change affects UHI and other indices, while water bodies impact LST in diverse ways depending on several variables. This emphasizes the need for individual investigations in various cities’ data from different observations/satellites.

This research analyses LST and UHI intensity in Pune city, India. It examines how water bodies affect LST fluctuations in the study region. Find out how different land use land cover affects UHI and other metrics. This study utilized remote sensing data acquired from NASA's website. Digital elevation models (DEMs) or digital terrain models (DTMs) provide information on the elevation and topography of the terrain in Pune. These data help in analyzing the slope, aspect, and relief of the region. GIS Infrastructure data include information about buildings, educational institutions, health facilities, government offices, and other important facilities in the city. Vegetation and green areas dataset focus on the distribution of vegetation cover, parks, gardens, and other green spaces in the city.

Data and study area

Authorized study area departments provided maps and master plans. Below are data, property, and pre-processing details. Figure 1 shows the topographical location of the study area. The city of Pune in the Indian state of Maharashtra serves as the study area for this study. Pune is in the western part of the country, at approximately 18.5204° North latitude and 73.8567° East longitude, as shown in Figure 1. It is the second-largest city in Maharashtra and is known for its historical and cultural significance. Pune is located on the Deccan Plateau, a large, elevated landmass in central India. The city is surrounded by hills and lies at an average altitude of about 560 m above sea level. The topography of Pune is characterized by a mixture of hilly regions, river valleys, and plains. The Mutha River flows through the city, dividing it into two major parts: Pune and Pimpri-Chinchwad. As far as data from GIS are concerned, Pune has made considerable progress in recent years. An extensive spatial data infrastructure has been developed in the city and various datasets are available. The Pune Municipal Corporation (PMC) and other government agencies have been actively involved in collecting and maintaining GIS data for urban planning, infrastructure management, and environmental assessments.
Figure 1

Study area.

The GIS data available for the city of Pune include Administrative Boundaries. This dataset contains the boundaries and subdivisions of the city, such as wards, constituencies, and administrative zones. The land use and land cover data from GIS classify the different areas of the city based on their use, e.g., residential, commercial, industrial, agricultural, and open space. Transport network dataset contains information on the road network, including major motorways, main roads, and local roads. It may also include data on public transport routes, bus stops, and railway lines. Hydrology & GIS data on water bodies such as rivers, lakes, reservoirs, and drainage networks help to understand the hydrological characteristics of the study area.

Thermal sensors of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8's OLI/TIRS and remote sensing data of linear imaging and self-scanning sensor (LISS) III and LISS-IV sensors on RESOURCESAT-1 and 2 have been used in this investigation. As shown in Table 1, the seven imaging bands (three visible, four infrared) of OLI sensors have 30 m spatial resolution. The OLI/TIRS instruments on Landsat 8 produced data with a spatial resolution of 30 m and a temporal frequency of 16 days, enabling reliable temperature change tracking over time. Additionally, the comprehensive dataset was expanded by sensors on RESOURCESAT-1 and 2 such as LISS-III and LISS-IV that provided data at intervals of 5–24 days with spatial resolutions as fine as 5.8 m. MODIS on the Terra and Aqua platforms allowed for the provision of a wide range of thermal and spectral data, spanning the Earth's surface every 1–2 days with varied spatial resolutions (250, 500, and 1,000 m) and temporal resolutions (daily to annual). MODIS' sweep covers 2,330 km of Earth every 1–2 days. It gathers data at 250, 500, and 1,000 m. MODIS on Terra and Aqua platforms offers 36 wavebands of visible, NIR, short-wave infrared (SWIR), and thermal Earth surface data.

Table 1

Input data set details

Band numberSensorTemporal resolutionSpatial resolution
Landsat OLI/TIRS 
Band 1 Blue 16 days 30 
Band 2 Green 30 
Band 3 Red 30 
Band 4 Near IR 30 
Band 5 Mid IR 30 
Band 6 Thermal 30 
Band 7 Mid IR 30 
Band 8 Panchromatic 15 
Band 9 Cirrus 30 
LISS-III 
B2 0.52–0.59 24 days 23.5 
B3 0.62–0.68 
B4 0.77–0.86 
B5 1.55–1.70 
LISS-IV 
B2 0.52–0.59 5 days 5.8 m 
B3 0.62–0.68 
B4 0.77–0.86 
MODIS 
MYD11A2 – 1 day 926.6 m 
Band numberSensorTemporal resolutionSpatial resolution
Landsat OLI/TIRS 
Band 1 Blue 16 days 30 
Band 2 Green 30 
Band 3 Red 30 
Band 4 Near IR 30 
Band 5 Mid IR 30 
Band 6 Thermal 30 
Band 7 Mid IR 30 
Band 8 Panchromatic 15 
Band 9 Cirrus 30 
LISS-III 
B2 0.52–0.59 24 days 23.5 
B3 0.62–0.68 
B4 0.77–0.86 
B5 1.55–1.70 
LISS-IV 
B2 0.52–0.59 5 days 5.8 m 
B3 0.62–0.68 
B4 0.77–0.86 
MODIS 
MYD11A2 – 1 day 926.6 m 

Land use analysis and cooling effect of water body

LISS-III and LISS-IV data have been obtained from the National Remote Sensing Centre (NRSC), India, with resolutions of 23.5 and 5.8 m. LPDAAC 1 km MODIS data and Survey of India 1:50,000 topo sheets have been utilized. ERDAS Imagine georeferenced topo sheets, master plans, and Ground control points (GCPs) were marked on maps and satellite images. A map and image transformation model reduced RMS error. Supervised classification using ERDAS IMAGINE 9.2's maximum likelihood approach mapped land cover patterns. Each land use category had confirmed training locations, and the categorization data were blended to get an average pattern. The ‘Accuracy assessment tool’ in ERDAS IMAGINE 9.2 analyzed classification accuracy by class, user, and producer. ArcGIS extracted land cover classes. LST The MYD11A2 8-day MODIS nighttime product has been used to analyze seasonal temperature trends for each land use/cover category in the research region in 2020 and 2023. The cooling effect of water bodies has been studied using the following methodology, as shown in Figure 2. Creation of buffers and transects created above and adjacent to the study objects, focusing on a nearby lake and river basin.
Figure 2

Research methodology.

Figure 2

Research methodology.

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LST estimations for LANDSAT data

The temperature of the land surface was taken from LANDSAT (TM) shots to make the LST profile for the whole study area. Before the images could be used, they had to go through preparation, which included radiometric and geometric changes. Using the equation and correction factors given, the digital numbers (DN) of the shots were turned to spectral light (L). Using the equation and adjusting factors given, change the spectrum reflection to the actual satellite temperature in Kelvin. Spectral emissivity correction based on the type of the surface; a correction was made for the spectral emissivity. Extraction of temperature for individual buffer regions was extracted from data at LST. Then, these numbers were mapped and looked at visually and graphically to see if there were any drops in temperature near bodies of water, which would show a cooling effect. The downloaded LANDSAT (TM) images are submitted to radiometric and geometric changes to ensure their usefulness for temperature research (Sharma et al. 2023). Conversion of the digital number (DN) to spectral radiance (L) using the following equation.
(1)
where Lλ is the Spectral radiance in watts/m2*ster*μm, LMAX is the maximum spectral radiance scaled to QCALMAX in watts/m2*ster *μm, LMIN is the minimum spectral radiance scaled to QCALMIN in watts/m2*ster * μm, QCALMAX is the maximum quantized calibrated pixel value in DN, QCALMIN is the minimum quantized calibrated pixel value in DN. Conversion of spectral radiance to satellite effective temperature (TB) spectral radiance numbers changed to the satellite effective temperature using the following equation:
(2)
where TB is the effective satellite temperature in Kelvin, K1 is the calibration constant 1 (666.09), K2 is the calibration constant 2 (1,282.71), Lλ is the spectral radiance calculated from the previous equation (Equation (1)). Correction for spectral emissivity has been using the following equation, the temperature values found in the previous step are changed by the spectral emissivity based on the type of surface.
(3)
where St is the land surface temperature, From the earlier calculation, TB is the temperature of a black body, λ is the Wavelength of the light wave that was released (11.5 m), = h * c = 1.438 × 10−2 mK (=Boltzmann constant = 1.38 × 10−23 J/K, h is the Planck constant = 6.626 × 10−34 Js, c is the speed of light). The end of this methodology is the estimate of the land surface temperature (St) in Kelvin. If you need to, you can change the temperature numbers from Kelvin to Celsius by taking 273 away from the Kelvin values. It is important to know that these mathematical models are used to estimate and analyze temperature by applying them to LANDSAT (TM) satellite images. The constants and formulae mentioned are specific and may vary for other sensors or satellite platforms.

The best way to understand the influence and contribution of land use/land cover on LST is to investigate the connection between thermal signatures (i.e., the LST profile of the study area) and land cover types. The most extreme and least surface temperatures for diverse times of distinctive year are profoundly variable, contingent upon the season of the year. The contribution of different land cover types also varies accordingly. Hence seasonal analysis is required for appropriate information. To ease the analysis the LISS-III and LANDSAT land cover maps have been aggregated from original resolution of 23.5–926.6 m and 30–926.6 m, respectively. LST from MODIS 8-day images for nighttime has been extracted out for different classes respectively, obtained from the LULC maps.

LST and LULC information on Pune's urban agglomeration has been processed using remote sensing's temporal and geographical data. The region has been divided into seven different LULC: residential, commercial, industrial, road, soil, vegetation, and water. By comparing data from 2020 to 2023, our analysis examined the nighttime LST fluctuations over the summer and winter seasons. Agriculture, forests, and parks are all included in the ‘Vegetation’ category, whereas ‘Soil’ includes bare ground and open spaces. To create land cover maps for both years by a supervised classification, i.e., maximum likelihood method. High-resolution data of 1-m spatial resolution obtained from Bhuvan Store has been used for categorization accuracy. The individual effects of these land use types on the UHI effect analysis and water cooling effect of water bodies have been determined. The variation in the proportion of built-up areas (% NDBI) for each land use category gave insights into how the dynamics of the urban landscape changed over the course of the 3 years.

Seasonal variations in surface temperature and vegetation

Observations from Figure 3 indicate the maximum LST during the summer and winter seasons is 303.08 and 289.9 K, respectively, while the absolute minimum temperature is 284.2 and 277.69 K, respectively. In 2020, the maximal LST ranges from 299.4 to 293.2 K in the summer and from 288.4 to 281.6 K in the winter. Similarly, the minimum LST values for 2020 range from 293.5 to 280.5 K during the summer and from 270.7 to 270.5 K during the winter. During 2023, the maximum LST during summer and winter ranges from 302.0 to 289.3 K and from 288.6 to 279.2 K, respectively, and the minimum LST ranges from 294.3 to 279.2 K in summer and 278.5 to 274.4 K in winter.
Figure 3

Seasonal variation of mean LST and UHI intensity for days number of (a) 2020 and (b) 2023.

Figure 3

Seasonal variation of mean LST and UHI intensity for days number of (a) 2020 and (b) 2023.

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The variation of mean LST and UHI intensities is known to vary during the day and throughout the year. Prominent temperature changes throughout the seasons, as shown in Figures 3 and 4, conclusively prove the existence of UHI. The highest UHI intensity was between 5 and 9 K, and it was observed in both the summer and winter of 2020 and 2023. For example, in 2020, the highest UHI intensity was 9.3 K in the summer and 8.75 K in the winter, whereas in 2023, these values were 7.39 and 8.55 K for the corresponding seasons.
Figure 4

LST variations for summer and winter seasons.

Figure 4

LST variations for summer and winter seasons.

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To make analysis easier, the original resolutions of the LISS-III and LANDSAT land cover maps have been changed from 23.5 to 926.6 m and 30 to 926.6 m, respectively, to 30 to 926.6 m. Also, the LST from MODIS 8-day images for nighttime has been pulled for different classes from both LULC maps. The best way to understand how land use and land cover affect and contribute to LST is to look at the link between temperature fingerprints (i.e., the LST profile of the study area) and types of land cover. Throughout the year, the hottest and lowest surface temperatures vary a lot depending on the season. In the same way, the input of each type of vegetation cover is different. Figure 5 shows the average LST for each LULC in 2020 and 2023. Figure 5 shows the changes in each type of land use affecting the temperature of the land area over time. In both summer and winter, the average temperature is highest in the commercial area, then in the residential area, followed by road and industrial area, whereas much less in the vegetation and water bodies for 2020 and 2023.
Figure 5

Seasonal mean LST by land use type in (a) 2020 and (b) 2023.

Figure 5

Seasonal mean LST by land use type in (a) 2020 and (b) 2023.

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Water has the lowest average LST, with only a small difference from vegetation. Between 2020 and 2023, different land use groups showed differing trends in the average LST. These findings persisted throughout the winter, along with an overall rise in LST. Increased temperatures were a result of the expanding industrial sector competing with populated areas. Due to their ability to release heat from the day, roads showed higher evening temperatures. The water class has the lowest average temperature in both summer and winter. It is safe to say that the average temperature has gone up over time because of how crowded and concentrated the residential areas were before and because of the future residential infrastructure project to make room for Pune's growing population.

When looking at the number of diverse types of space used on a regular and yearly basis, another interesting trend emerges. For both summer and winter, the average temperature of each group has gone up. For summers, the average LST has gone up in residential areas from 292.3 to 295.5 K, commercial areas from 292.8 to 293.9 K, industrial areas from 295.7 to 296.2 K, roads from 293.4 to 295.3 K, soil from 292.8 to 293.7 K, vegetation from 293.5 to 294.1 K, and water from 292.3 to 295.4 K. In the winter, the average temperature of the land surface goes up in every category: residential 283.4–285.5 K, commercial 282.4–284.3 K, industrial 283.7–285.3 K, roads 282.4–282.18 K, soil 278.05–280.67 K, vegetation 279.32–281.45 K, and water 277.9–284.1 K. From 2020 to 2023, the average LST went up more in the winter than in the summer. This shows that the area has been getting warmer over time due to things like impervious surfaces, human activities, and the loss of green cover, among other things.

Figures 6 and 7 show the distribution of impervious surface areas with values between 0 and 100% across a variety of land cover types and time periods using NDBI imagery. Roads, parking lots, commercial spaces, industrial areas, and other impervious surfaces make up the bulk of urban landscapes. Figure 6 shows impervious surface differences over two research periods.
Figure 6

NDBI variations for summer and winter seasons.

Figure 6

NDBI variations for summer and winter seasons.

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Figure 7

NDBI variations over Pune by land use/cover type of 2020 and 2023.

Figure 7

NDBI variations over Pune by land use/cover type of 2020 and 2023.

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Despite a LULC map with seven categories, water bodies are not included in the imperviousness study. It was reclassified as non-urban after having 0% imperviousness. The NDBI trend indicates a rising imperviousness over time. Roads, soil, commercial, residential, industrial, and finally vegetation make up the smallest percentage of the built environment. Roads, soil, commercial area, residential area, industrial area, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. The NDBI parameter, which considers impervious surfaces and anthropogenic influences such as those from industry, air conditioning, and transportation, rises as urbanization progresses. In previous studies, a graph comparing LST and NDBI indicates a rising trend and a positive correlation coefficient (R2); this finding is supported for the present study as well, and it will help us understand the primary cause of the temperature rise in the study area over time. The rising prevalence of hardscapes is a major contributor to the higher-than-average surface temperatures recorded by the NDBI in the Pune study region.

Terrain analysis for UHI

Figure 8 shows the aspect map of Pune city. Aspect maps provide useful information for mapping UHI intensity in Pune. The spatial variations in UHI intensity within a city can be determined by analyzing aspect data. The aspect map of the city of Pune will classify various slope directions according to predefined angular ranges. Each aspect class corresponds to a particular incline orientation, including north, south, east, west, north-east, north-west, south-east, and south-west. On the map, these classifications are represented by distinct colors or symbols. Aspect maps depict the direction that gradients in a particular area face. It is also derived from a DEM and represents the cardinal orientation of each slope. Aspect maps can assist in identifying the spatial distribution of slopes facing various directions within the context of UHI intensity mapping. This information is essential for comprehending how solar radiation interacts with the urban environment, as numerous factors can affect the quantity and duration of sunlight received by various areas. By analyzing the aspect map, researchers can evaluate the potential influence of slope direction on the UHI effect, such as variations in surface heating and cooling rates.
Figure 8

(a) Aspect, (b) contour, (c) DEM, and (d) slope maps of Pune city.

Figure 8

(a) Aspect, (b) contour, (c) DEM, and (d) slope maps of Pune city.

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On a contour map, lines (contour lines) connecting points of equal elevation on a DEM are displayed. These lines depict the terrain's form and relief. In the context of UHI intensity mapping, a contour map of Pune can offer a more intuitive visual representation of the city's elevation profile. Contour lines can reveal areas of higher or lower elevation, identify ridges, valleys, and plateaus, and aid in the comprehension of the spatial distribution of topographic features. Frequently, contour maps are combined with slope and aspect maps to provide a comprehensive comprehension of the terrain and its effect on the intensity of UHI.

A slope map depicts the inclination or steepness of the terrain in each area. It is derived from a DEM and measures the rate of elevation change between adjacent cells. A slope map can provide valuable information about the city's topography, including areas with varying degrees of slope, in the context of UHI intensity mapping. By affecting wind patterns and surface discharge, steeper gradients can influence the local climate. Different slope angles can result in variations in solar exposure and surface temperature, which can influence the distribution of urban heat. A DEM map is a digital representation of the Earth's surface topography. It provides elevation values for each grid cell, allowing for the visualization and analysis of terrain features. In the context of UHI intensity mapping, a DEM of Pune can provide an accurate representation of the city's elevation profile. It assists in identifying variations in landforms, such as hills, valleys, and flat areas, which can influence local climate and the formation of UHIs. The DEM can be utilized to generate slope and aspect maps and to support additional UHI-related analyses, such as land cover classification, urban morphology, and surface temperature modeling.

Aspect, slope, DEM and contours maps play a significant role in determining the quantity of solar radiation received by various urban areas. South-facing slopes receive an increased amount of direct sunlight throughout the day, resulting in higher temperatures. Alternatively, north-facing slopes receive less direct radiation and are typically colder. By analyzing the aspect map, areas with south-facing elevations that are likely to have higher UHI intensities can be identified. The aspect map assists in comprehending the temperature variations across various slope orientations. South-facing slopes are exposed to a greater amount of solar radiation and tend to retain heat, leading to higher temperatures. North-facing slopes receive less solar radiation and are consequently colder. By superimposing temperature data on the aspect map, it is possible to identify regions with varying UHI intensities.

The Aspect, slope, DEM, and contours maps have disclosed urban areas with heat accumulation and dissipation. Densely populated areas with south-facing elevations, for instance, may retain heat, leading to higher UHI intensities. Alternatively, open spaces and areas with north-facing slopes may facilitate heat dissipation, resulting in reduced UHI intensities. It can identify regions with more pronounced UHI effects, allowing for targeted urban planning, green infrastructure development, and heat mitigation measures.

Water cooling effect analysis in Pune

Six images, one each from summer and winter in 2020 and 2023, have been chosen at random from the available data for study. Figure 9 displays NDWI seasonal trends from research.
Figure 9

Seasonal NDWI map of Pune city.

Figure 9

Seasonal NDWI map of Pune city.

Close modal

Pune, a fast urbanizing city, is recognized for its lakes, which contribute to the ecology and mitigate the UHI impact. Pune's western Pashan Lake is a residential recreation area. Pashan Lake can localize cooling. Lakes absorb heat throughout the day and release it slowly at night. This lowers lake surface temperatures and moderates the UHI impact. Khadakwasla Lake is Pune's famous lake that lies on the outskirts. It supplies the city with water and attracts tourists. Khadakwasla Lake's vast water may moderate local temperatures. The lake's evaporative cooling and local winds may chill the microclimate and reduce UHI severity. Hills and trees surround Katraj Lake in southern Pune. Lake vegetation may reduce the UHI impact. Water and vegetation increase evapotranspiration, cooling the air. This cooling effect may reduce UHI by cooling neighboring metropolitan areas. Pune has various minor rivers and streams, including Ram Nadi and Ambil Odha. These waterbodies may mitigate UHI like lakes. Flowing water may chill the nearby microclimate via evaporation.

Lakes and water bodies may reduce the UHI impact, although their efficiency depends on size, depth, water quality, adjacent land cover, and urban development patterns. These water bodies must be managed and preserved to mitigate UHI in Pune. Promoting green areas and water bodies in urban development may improve the city's microclimate and inhabitability as shown in Figure 10.
Figure 10

Mean summer/winter LST (K) of 2020 and 2023 by varying buffer distance from left/right bank of water bodies: (a) 2020 Summer, (b) 2020 Winters, (c) 2023 Summers, and (d) 2023 Winters.

Figure 10

Mean summer/winter LST (K) of 2020 and 2023 by varying buffer distance from left/right bank of water bodies: (a) 2020 Summer, (b) 2020 Winters, (c) 2023 Summers, and (d) 2023 Winters.

Close modal

The analysis of seasonal NDWI maps in conjunction with surface temperature data can reveal the influence of lakes on temperature variations. In the case of Pune city, the temperature profile along a transect from the lake's margin can disclose the lake's effect on local temperatures. The graph is likely to demonstrate that the lake has a chilling influence on its immediate surroundings during the summer months. On the right side of the lake, temperatures are anticipated to be lower within approximately 900 m compared to areas further away. This indicates that the lake contributes to the cooling effect within this temperature range. Beyond this point, however, the temperature begins to progressively rise as the lake's influence diminishes.

In general, temperatures tend to be higher in mountainous terrain (Sharma et al. 2020b). It is therefore conceivable that the temperature profile graph will indicate elevated temperatures in the hilly regions surrounding the lake, even within the 900-m range. The topography of the region contributes to the creation of milder conditions in these regions. On the left side of the transect, approximately 1,200 m distant from the lake, it is anticipated that the temperature profile will exhibit minimal variation. This suggests that, beyond this distance, the lake's influence on temperature decreases and the adjacent land has a greater impact on temperature patterns. It is essential to observe that winter conditions affect both sides of the 900-m range. During the winter, the lake's chilling effect may be diminished, and temperatures on both sides of the transect may fluctuate similarly due to seasonal weather conditions. By analyzing the temperature profile along the transect and considering the influence of the lake, topography, and seasonal variations, we can obtain a clearer understanding of how the lake affects Pune's local temperature regime.

The association between urban waterways and surface temperature has been done using Landsat-7, Formosat-2, and ground observation data. Metro Taipei. The high-resolution satellite image classifies Taipei's water, barren ground, herbal, building, and vegetation areas. The research used ISODATA's unsupervised classification algorithm to derive the LST for land use. The research found that the river may effect on natural region for 300 m; 160 m when marshes exist between the river and the levee. If there are simply flooded plains in between, the range may be −19 to 71 m, where the negative figure suggests the surface temperature stabilizes before the levee. The river surface temperature is also impacted by the extent of the city-river buffer zone. Another research in Nanjing, China found that lakes, rivers, and seas cool metropolitan climates. The Ota River in Hiroshima, Japan, cooled the air by 5 °C right above the river and approximately 100 m from its banks. These studies show rivers can cool hot areas. LST and water use/cover using Landsat TM data. A land cover classification by the method of supervised classification establishes classes as urban, forest, cropland, bare land, and water. The built-up region has an elevated temperature (32.780 °C), whereas the water body with the lowest temperature (25.290 °C) may mitigate the LST. Reducing the UHI effect demands sustainable and climate-responsive urban development planning and design. Land use planning promotes mixed residential, commercial, and institutional spaces in residential areas. This method decreases long-distance commuting, promotes walking, and lowers heat accumulation. Compact development and increased density reduce heat-absorbing surface surfaces and maximize infrastructure and service efficiency.

According to the numbers, the temperature drops significantly over the river, often by about 10 °C when the ambient air temperature is over 20 °C. This was obvious during the day but not at night because of the dip in temperature. Late in June, although having the same air temperature, there was less cooling. The larger the body of water, the greater the impact. This is not a linear connection, either, since even a tiny quantity of water may have a significant cooling effect. Water features have been strategically placed by urban designers and builders to moderate temperatures throughout. To maximize the cooling effects of water bodies and urban development, one must consider these factors. UHI mitigation requires green space planning. Trees, shrubs, and green roofs shade and lower surface temperatures by evapotranspiration. Parks, forests, and wetlands chill and shade the city when integrated. Green corridors interconnected networks of green spaces and vegetation improves airflow and reduces heat buildup. Sustainable design reduces urban heat islands. Light-colored buildings, reflecting pavements, and roofs reduced heat absorption and surface temperatures. Rain gardens, bioswales, and permeable pavements reduce stormwater runoff and cool the environment. Natural ventilation promotes airflow and cross-ventilation, eliminating the need for energy-intensive air conditioning.

Monitoring and modeling UHI patterns and hotspots are essential to combating it. Heat island studies identify specific intervention areas. Urban microclimate modeling can mimic planning and design initiatives to inform decision-making. Urban planners, architects, engineers, legislators, and communities must collaborate to mitigate the UHI effect. Integrated planning, design, and public participation can reduce the heat island effect and make cities more sustainable and resilient.

Metropolitan and emerging cities experience the UHI effect. Numerous studies have examined this influence and its causes, although most have focused on cities in richer and colder countries. Few studies have examined UHI in rapidly expanding Indian cities. City planning and infrastructure development need understanding how land uses affect UHI. Land use type growth has been studied more than its UHI effects. This study analyses land use temperature, UHI, and surface imperviousness for each land use type to illustrate this discrepancy. The study examines the impacts of land use and land cover on UHI over summer and winter seasons from 2020 to 2023 using 8-day nighttime MODIS LST data. Residential, commercial, industrial, roads, soil, vegetation, and water comprise the study area. The study found substantial Surface Urban Heat Island (SUHI) impacts in all three cities by tracking LST across these categories over 2 years. The residential sector had the highest mean LST for summer and winter seasons in Pune in 2020 and 2023. Residential regions have the greatest temperatures in 2020, while industrial sectors are warmer in 2023, while the difference is small. During the investigation, the soil had the lowest temperature in all three cities. Soil absorbs less heat than artificial materials, making metropolitan areas with artificial surfaces warmer.

Water bodies also affect UHI, the study found. The research also evaluates NDBI growth for all land use groups, which may explain the 2020–2023 temperature rise. LANDSAT TM data are used to study water bodies' cooling effects. Lakes in Pune significantly chill the air. The LST graph shows a temperature reduction within 1.2 km of surrounding Lakes, encompassing the lake's 600 m width, with summer and winter variations. The left side of the transect is cooled up to 1,200 m, whereas the right side is 900–1,200 m. The research also found that Pune waterways had greater evening temperatures than highways and sometimes water bodies seem to exacerbate the UHI effect rather than chill it. The analysis also shows that winter temperatures increased across all land use categories from 2020 to 2023. The average summer LST in Pune rose 1 and 2 °C, respectively, from 2020 to 2023. Future research in Pune should concentrate on long-term UHI trends, the impact of urban green spaces, socio-economic factors, adaptation to climate change, comparative analysis, remote sensing applications, and stakeholder engagement for policy implications.

The corresponding author takes responsibility on behalf of all authors for ethical approval and permissions related to this research work.

The corresponding author takes responsibility on behalf of all authors for consent to participate related.

All the parties gave their written permission for the article to be published. The corresponding author takes responsibility on behalf of all authors for consent to consent to publish.

All authors added to the idea and planning of the study. Vijendra Kumar put together the materials and gathered the data. The modeling was done by Dr Kul Vaibhav Sharma. The text was written by Lilesh Gautam. Sumit Choudhary did the work of analyzing. Aneesh Mathew helped with making graphs and supervised the whole project. All authors have read the final draught and agreed with it.

The corresponding author on behalf of all authors declares that no funds or any other grant received during the preparation of this manuscript.

The datasets generated during the current study are available from the corresponding author on reasonable prior request.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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