'Urban Heat Island' study for Mumbai using Remote Sensing
- Arpit Shah
- May 3, 2020
- 4 min read
Updated: 21 hours ago
During a cricket match telecast, you may have heard something like:
"The temperature in Mumbai is 32 degrees, but it feels much warmer here at the Wankhede…"
Lest you think otherwise, the commentators may actually be spot on—they could be experiencing the Urban Heat Island (UHI) effect. Due to factors such as stadium layout (which obstructs winds), high-density infrastructure, concrete surfaces, and even crowd-generated heat and noise, the temperature within the stadium could indeed be a few degrees higher than the average recorded temperature of the city.
Technically, an Urban Heat Island refers to a condition where there is a significant temperature difference between an urban area and its surrounding non-urban areas at a given point in time.
This phenomenon occurs because urban areas generate, absorb, and retain more heat than their non-urban surroundings. Contributing factors include the density of infrastructure, heat-retaining building materials, air pollution, air-conditioning exhaust, traffic emissions, and more. Here’s a useful National Geographic explainer. A high UHI is indicative of excessive urbanisation and is associated with heat-related medical risks such as dehydration and elevated blood pressure.
As you can imagine, UHI data is immensely valuable for urban planners, architects, and environmental policymakers, enabling them to design infrastructure sustainably and adopt mitigation measures such as using eco-friendly construction materials or increasing urban green cover. The information is just as useful for air-conditioner manufacturers, who may expand their retail footprint or adjust inventory planning in zones with high UHI.
The methodology of deriving UHI can be accessed here. Below is the final map-based output—a classification of UHI for Mumbai at a Ward level as on 22 May 2019, extracted using Sentinel-3B thermal satellite imagery.
From the output, the maximum UHI is 1.59°C, which may not appear alarming at first glance. However, note that this temperature differential is an average at Ward level.
At a more granular pixel level (spatial resolution of SLSTR instrument onboard Sentinel-3 is ~1 km in the thermal infrared channel), the maximum UHI observed was significantly higher — about 3.0°C.
I refrained from presenting pixel-level results because the visualization becomes visually chaotic; therefore, I aggregated UHI to the more interpretable Ward level.
Additionally, the UHI effect is more prominent at night because urban areas retain the heat accumulated during the day, whereas non-urban areas cool down quickly. Unfortunately, S-3B imagery for nighttime was unavailable at the time, so I worked with an afternoon dataset (captured at 16:10 hours).
As a result, my maximum UHI estimate is conservative. I would not be surprised if nighttime UHI in Mumbai reaches ~7°C or even more on certain days. For example, if the daytime temperature in a mixed urban–non-urban region is 35°C, the non-urban nighttime temperature may drop to 25°C, but the urban zone may only cool to ~32°C due to heat retention.
Having lived in Mumbai for several years, the output in Figure 1 also aligns with intuitive knowledge. Large swathes of the main metropolis—from Colaba to Sion—have the darkest UHI shades. These are dense city zones with extensive infrastructure and minimal green cover.
Further north, suburban settlements such as Vile Parle and Andheri also show high UHI, unsurprising given their population density and built-up environment. By contrast, regions to the east of Eastern Express Highway and areas comprising Sanjay Gandhi National Park (near Borivali) show comparatively lower UHI due to lower urban density and the presence of natural landscapes.
However, some visual anomalies must be acknowledged. For instance, the central suburbs of Powai, Vikhroli, Bhandup, and Goregaon appear to have lower UHI than similarly dense settlements like Vile Parle and Andheri. This is because ward-level averaging pulls down the mean UHI due to major low-density features such as Powai Lake and Aarey Colony.
What else do you observe from the UHI output? Can you think of new ways in which such UHI datasets could be useful? Do let me know.
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