Mean Land Surface Temperature (LST)
Monthly land surface temperature estimates (°C) from Landsat 4–8, integrating surface reflectance, emissivity, and atmospheric data for high-resolution monitoring.
Description
This indicator calculates the mean land surface temperature (LST) using the Statistical Mono-Window (SMW) algorithm applied to Landsat thermal data (Landsat 4, 5, 7, and 8). It integrates surface reflectance, brightness temperature, and surface emissivity (from ASTER GEDv3), combined with atmospheric water vapour data (TCWV) from NCEP/NCAR reanalysis, processed via Google Earth Engine (GEE).
The methodology provides high spatial resolution (~30–100 m), enabling precise monitoring of temperature dynamics in urban, agricultural, and natural environments. Results have been validated against in-situ meteorological stations, showing high accuracy and reliability for both scientific and operational applications.
What it measures
- Monthly mean land surface temperature in °C.
- Variation across space and time at fine resolution.
- Temperature dynamics across different land cover types.
How to interpret
- Higher values: hotter surfaces (e.g., urban heat islands, exposed soil).
- Lower values: cooler surfaces (e.g., vegetated or shaded areas).
- Trends over time indicate impacts of climate change, deforestation, and land use.
Unit / Scale
Degrees Celsius (°C).
Temporal coverage: 2022–2024.
Spatial resolution: 30 m.
Update frequency: Monthly.
Author / Source
Ermida, S. L., Soares, P., Mantas, V., Göttsche, F., & Trigo, I. F. (2020). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sensing, 12(9), 1471.
🔗 https://www.mdpi.com/2072-4292/12/9/1471
Applications in Atlas
- Monitor heat stress in ecosystems and urban areas.
- Assess climate change impacts at regional and local scales.
- Support studies on land degradation, agriculture, and biodiversity.
- Provide high-resolution temperature data for environmental modelling.