How has the urban heat island in my city changed over decades?

What you can answer

• Multi-decade LST trend for urban core vs surrounding rural (MODIS Terra 2000+)
• Diurnal heat-island intensity (day vs night, MODIS Terra morning + Aqua afternoon)
• Hyper-local heat hotspots at 70 m (ECOSTRESS) — playgrounds, asphalt parking lots, factories
• Urbanization correlate — Black Marble nighttime lights expansion paralleling LST increase
• Heat impact on environmental-justice corridors — overlay LST with demographic data

What you can NOT answer with these alone

• Surface air temperature (LST is the skin temperature, can differ from 2-m air temperature by 5-15 K)
• Indoor heat exposure
• Heat-island mitigation effect of specific interventions without ground-truth measurement campaigns

Code template

import earthaccess
import xarray as xr
import numpy as np

earthaccess.login(strategy="netrc")

# Example: Chennai, India
city_aoi = (80.15, 12.95, 80.35, 13.20)
rural_aoi = (80.45, 12.95, 80.65, 13.20)  # ~25 km east as control
window = ("2000-03-01", "2025-12-31")

# 1. MODIS Terra daytime LST (10:30 AM overpass)
mod_lst = earthaccess.search_data(short_name="MOD11A1", bounding_box=city_aoi,
                                  temporal=window)
# Open + extract LST_Day_1km; spatial mean over city, rural
# Filter QC flag (use LST_Day_1km_QC; only QC=0 quality)

# 2. Compute urban-rural temperature difference per day → annual mean → trend
# 3. MODIS Aqua nighttime LST (1:30 AM overpass) — usually larger UHI
myd_lst = earthaccess.search_data(short_name="MYD11A1", bounding_box=city_aoi,
                                  temporal=window)

# 4. ECOSTRESS 70m for the modern era (2018+)
eco = earthaccess.search_data(short_name="ECO2LSTE", bounding_box=city_aoi,
                              temporal=("2018-08-01", "2025-12-31"))
# Map current spatial pattern at 70m

# 5. Plot:
#    - 25-year line: city minus rural LST anomaly
#    - Map: ECOSTRESS 70m LST overlaid on city map (highlight hot pockets)
#    - Trend bar: annual mean UHI intensity

Expected output

• Decadal trend line: urban-rural LST difference 2000-present (degrees K)
• High-resolution map: ECOSTRESS 70m LST for the current year showing within-city variability
• Comparison: day vs night UHI intensity
• Overlay: Black Marble nighttime lights growth on top of LST trend

Caveats

• LST is skin temperature, not air temperature — for human-heat-exposure work, model the conversion or combine with weather-station data
• Cloud screening eliminates many days — annual aggregates smooth this
• QC flag is critical; cloudy pixels get assigned bad values that look reasonable until averaged
• Pixel mis-registration at 1 km can blur urban-rural transitions; use ECOSTRESS for sharper boundaries
• ECOSTRESS has variable revisit + no pre-2018 record — use MODIS for the long trend

Cross-DAAC composition

LP DAAC for MODIS LST + ECOSTRESS — single DAAC; uniform Earthdata Login.

Sources

• MOD11A1 user guide: https://lpdaac.usgs.gov/products/mod11a1v061/
• ECOSTRESS science: https://ecostress.jpl.nasa.gov/
• Black Marble: https://blackmarble.gsfc.nasa.gov/