Urban heat
Heat in cities as data: land surface temperature from Landsat and MODIS, heat islands by day and night, green space, air temperature, heatwaves and who is exposed.
A Landsat heat map of a city is a map of surface temperature: the radiative skin of roofs, roads, tree crowns and dry grass at about eleven in the morning on one clear day. It is real and it is useful, and it is not the air anyone breathes. On 29 July 2023 in Madrid, built-up surfaces were 12 °C above the air at the airport and the hottest land cover was not the city at all but unirrigated grassland, at 46 °C.
Most of what goes wrong comes from reading a surface, a moment or a reference as something else. Madrid’s surface heat island is −3 °C by day and +5 °C by night; its sign at late morning depends on whether the rural reference is farmland or woodland. Its densest neighbourhoods have the coolest surfaces, and its small parks are cooler inside than its large ones, because what cools is trees and water, not the word park. The air at the airport says the heat that matters for health comes at night, which the satellite never sees.
The concepts below settle what LST is, the heat island, Landsat’s thermal record, emissivity, green space and exposure; the how-to guides work through real Landsat and MODIS scenes, weather-station records, reanalysis and population grids for Madrid; and the fixes are the five ways heat maps go wrong while looking vivid.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Read Landsat Surface Temperature in Python
- How to Build a Summer Land Surface Temperature Composite in Python
- How to Calculate Surface Urban Heat Island Intensity in Python
- How to Relate Land Surface Temperature to Vegetation in Python
- How to Measure the Cooling Effect of Parks in Python
- How to Summarise Land Surface Temperature by Neighbourhood in Python
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Learning paths through this topic
A curated route rather than a list — the guides in the order that builds on itself.
Related topics
Subjects that keep coming up alongside this one, most overlap first.