Thermal · Land Surface Temperature

LST_DAY – Land Surface Temperature (Daytime)

LST_DAY represents the daytime land surface temperature derived from thermal infrared bands. It is widely used for urban heat island studies, drought and energy balance applications.

1. Definition

Land Surface Temperature (LST) is the radiative skin temperature of the land surface, retrieved from thermal infrared (TIR) satellite measurements.

General Concept

For many products (e.g. MODIS LST), temperature is stored in Kelvin with a scale factor and then converted to °C:

LST (°C) = LST_raw × scale − 273.15

  • LST_raw – original LST band value
  • scale – product scale factor (e.g. 0.02 for MODIS)

Typical Interpretation

  • Higher LST → hotter surfaces (e.g. bare soil, built-up, asphalt)
  • Lower LST → cooler surfaces (e.g. vegetation, water, irrigated land)

Applications

  • Urban Heat Island (UHI) analysis
  • Agricultural and drought monitoring
  • Energy balance and evapotranspiration models
  • Climate and land–atmosphere interaction studies

2. Example Datasets

MODIS (used in the code below)

  • Product → MOD11A2.061 (Terra, 8-day, 1 km)
  • Band → LST_Day_1km
  • Scale factor → 0.02
  • Units → Kelvin (after scale), then converted to °C

Other Options

  • Landsat LST (from thermal bands B10/B11)
  • ECOSTRESS LST
  • ERA5-Land LST (reanalysis)

Suggested Palette (°C)

[ "#313695", "#4575b4", "#74add1", "#fdae61", "#f46d43", "#a50026" ]

3. Google Earth Engine Code – LST_DAY from MODIS


// LST_DAY – Land Surface Temperature (Daytime)
// Example: MODIS MOD11A2 Daytime LST (8-day, 1 km)

// AOI
var roi = geometry;
Map.centerObject(roi, 6);

// 1. Load MODIS LST (Terra, 8-day, 1 km)
var lstCol = ee.ImageCollection("MODIS/061/MOD11A2")
  .filterBounds(roi)
  .filterDate("2023-01-01", "2023-12-31")
  .select("LST_Day_1km");

// 2. Convert to Celsius
// MODIS scale factor = 0.02, original units Kelvin
function toCelsius(img) {
  var lstK = img.select("LST_Day_1km").multiply(0.02);
  var lstC = lstK.subtract(273.15).rename("LST_DAY_C");
  return lstC.copyProperties(img, img.propertyNames());
}

var lstCCol = lstCol.map(toCelsius);

// 3. Aggregate over time (e.g. annual mean daytime LST)
var lstDayMean = lstCCol.mean().clip(roi);

// 4. Visualization (°C)
var vis = {
  min: -10,
  max: 50,
  palette: [
    "#313695", "#4575b4", "#74add1",
    "#fdae61", "#f46d43", "#a50026"
  ]
};

Map.addLayer(lstDayMean, vis, "LST_DAY (Mean °C, 2023)");

// 5. Export LST_DAY
Export.image.toDrive({
  image: lstDayMean,
  description: "LST_DAY_export",
  fileNamePrefix: "LST_DAY_MODIS_2023_mean",
  region: roi,
  scale: 1000,   // MODIS ~1 km
  crs: "EPSG:4326",
  maxPixels: 1e13
});