Land Surface Temperature (LST) is a crucial parameter in environmental monitoring, climate studies, and urban heat island analysis. With the availability of Landsat 8 Collection 2 Level 2 data, extracting LST has become more accurate and efficient. In this blog, we will walk you through a simple and easy method to calculate and map LST using ArcMap.
Check out the full tutorial video here:

Why Calculate Land Surface Temperature?

Understanding LST helps in:
✔ Climate Change Studies – Analyzing temperature variations over time.
✔ Urban Heat Island Effect – Monitoring temperature differences in urban vs. rural areas.
✔ Agriculture & Drought Monitoring – Assessing crop health and soil moisture conditions.
✔ Disaster Management – Evaluating heat stress and wildfire risks.

Step-by-Step Guide to LST Mapping in ArcMap

1. Download Landsat 8 Collection 2 Level 2 Data
   > Go to USGS Earth Explorer to download the required Landsat 8 Surface Temperature (ST) Band 10 data.
   > Ensure you select the Collection 2 Level 2 dataset, which includes pre-processed atmospheric correction.

2. Load the Data into ArcMap –> Open ArcMap and add the Band 10 (Surface Temperature Band) raster layer.
3. Apply the LST Formula
   > The LST formula provided in the Landsat 8 metadata is:
   T (°K) = DN*ML+AL
   DN = SR pixel values
   ML = Multiplicative rescaling factor (also called the gain)
   AL = Additive rescaling factor (also called the offset)
   
4. To convert from Kelvin to Celsius:
   T (°C) = T (°K) – 273.15

Use the Raster Calculator in ArcMap to apply this equation.

Visualize and Interpret the LST Map
>Use Symbology settings to classify the temperature values.
>Apply a color gradient (e.g., blue for low temperature, red for high temperature).
>Compare the results with different land cover types

Conclusion

This simple method allows anyone to calculate and map LST using ArcMap efficiently. Whether you are a GIS professional, researcher, or student, this technique will enhance your understanding of thermal remote sensing and environmental analysis.
Let us know in the comments if you have any questions or need further assistance! 🚀

Stay tuned for more GIS and Remote Sensing tutorials!

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