Thermal Remote Sensing • Nighttime LST

LST_NIGHT – Nighttime Land Surface Temperature

LST_NIGHT represents the land surface temperature at night, usually retrieved from thermal infrared satellite observations. It is essential for studying urban heat islands, nocturnal cooling, energy balance, and thermal comfort in cities and natural landscapes.

Unit: °C (degrees Celsius) • Source example: MODIS Nighttime LST (MOD11A1)

1. Concept & Definition of LST_NIGHT

LST_NIGHT is the land surface temperature measured during the satellite night-time overpass. It is derived from the thermal infrared (TIR) emission of the surface and the atmosphere, often using physical models or single/dual channel algorithms.

Unlike simple spectral indices based on visible/NIR bands, LST_NIGHT is usually obtained from a dedicated surface temperature product (e.g., MODIS MOD11A1), where atmospheric correction and emissivity are already accounted for.

Typical Representation

In many products (e.g., MODIS MOD11A1), the nighttime LST is provided as:

LST\_Night\_Celsius = LST\_Night\_1km × 0.02 - 273.15 from scaled Kelvin to °C

Where LST_Night_1km is the original band (scaled integer), multiplied by 0.02 to get Kelvin, then converted to Celsius by subtracting 273.15.

Typical Nighttime LST Ranges (°C)

LST_NIGHT Range (°C) Interpretation
< 5 °C Very cold surfaces at night (high latitude, winter, mountains)
5 – 15 °C Cool conditions, typical of rural or vegetated areas at night
15 – 25 °C Mild to warm nighttime temperatures
25 – 30 °C Hot nights, often associated with strong urban heat island
> 30 °C Extremely hot nights, thermal stress and low cooling potential

Main Applications

  • Urban heat island mapping and nighttime cooling analysis
  • Thermal comfort and climate resilience studies
  • Heatwave monitoring and risk assessment
  • Surface energy balance and evapotranspiration models
  • Comparison of rural vs urban thermal regimes

2. Data & Bands for LST_NIGHT

Common Sensors & Products

  • MODIS (Terra/Aqua) – MOD11A1 / MYD11A1
    • LST_Night_1km – Nighttime LST band (scaled, 1 km)
    • QC_Night – Quality flags for LST_Night
  • VIIRS – Nighttime LST products (various collections, ~750–1000 m)
  • Other thermal missions – e.g. ECOSTRESS, Sentinel-3 SLSTR (depending on availability)

Good Practice

  • Prefer official LST products that already include atmospheric correction and emissivity.
  • Use QC (quality) bands to filter out low-quality or cloudy pixels.
  • Aggregate several days (e.g. median over 1–4 weeks) to reduce noise and missing data.
  • Re-project to a common CRS and resolution if combining with other indices (NDVI, NDBI, etc.).

Notes on Units

Many LST products store temperature as a scaled integer. For MODIS LST:

  • Scale factor = 0.02
  • Original values in Kelvin → convert to °C by subtracting 273.15

3. Google Earth Engine Code – LST_NIGHT (MODIS) for Any AOI

Steps: open code.earthengine.google.com → New Script → paste the code → draw your AOI as geometry on the map → click Run. Then export nighttime LST as GeoTIFF to Google Drive. 

// LST_NIGHT (Nighttime Land Surface Temperature in °C) for any AOI
// using MODIS MOD11A1 (Terra) daily product
// ---------------------------------------------------------------
// 1) Go to: https://code.earthengine.google.com
// 2) Click "New Script" and paste this code.
// 3) On the map: draw your AOI (Polygon/Rectangle).
//    It will appear as a variable named 'geometry' in the left panel.
// 4) Click "Run" to display LST_NIGHT.
// 5) In the Tasks tab, click "Run" to export LST_NIGHT to Google Drive.

// -------------------------------------------------------
// 1. Define Area of Interest (AOI)
// -------------------------------------------------------
var roi = geometry;  // Make sure a 'geometry' object exists in the left panel

// Center the map on the AOI
Map.centerObject(roi, 7);

// -------------------------------------------------------
// 2. Select MODIS LST product & time range
// -------------------------------------------------------
// Use MODIS Terra LST product (Collection 6.1)
var startDate = '2023-06-01';
var endDate   = '2023-06-30';

var modis = ee.ImageCollection('MODIS/061/MOD11A1')
  .filterBounds(roi)
  .filterDate(startDate, endDate);

// -------------------------------------------------------
// 3. Prepare Nighttime LST in Celsius
// -------------------------------------------------------
// LST_Night_1km is delivered as scaled integer:
//   LST(Kelvin) = DN * 0.02
//   LST(°C)     = LST(Kelvin) - 273.15

var lstNightK = modis
  .select('LST_Night_1km')
  .median()
  .clip(roi);

var lstNightC = lstNightK
  .multiply(0.02)
  .subtract(273.15)
  .rename('LST_NIGHT_C');

// -------------------------------------------------------
// 4. Visualization parameters
// -------------------------------------------------------
var lstVis = {
  min: 5,   // °C
  max: 35,  // °C
  palette: [
    '#0d0887', // cold
    '#3b528b',
    '#5ec962',
    '#fde725', // warm
    '#f97316', // very warm
    '#b30000'  // extreme
  ]
};

// Add LST_NIGHT layer to the map
Map.addLayer(lstNightC, lstVis, 'LST_NIGHT (°C)', true);

// Optionally, display a hillshade or background basemap
Map.addLayer(ee.Image().paint(roi, 0, 2), {palette: ['white']}, 'AOI boundary', false);

// -------------------------------------------------------
// 5. Export LST_NIGHT as GeoTIFF to Google Drive
// -------------------------------------------------------
Export.image.toDrive({
  image: lstNightC,
  description: 'LST_NIGHT_Export',
  fileNamePrefix: 'LST_NIGHT_Export',
  region: roi,
  scale: 1000,     // MODIS native resolution ~1 km
  crs: 'EPSG:4326',
  maxPixels: 1e13
});

// -------------------------------------------------------
// Notes:
// - You can change startDate / endDate for a different period.
// - You can adjust the visualization range (min/max) to your climate region.
// - If needed, use QC_Night band to mask low-quality pixels.
// -------------------------------------------------------