Satellite image processing for LST in Python

Dear Geocoders,
we have the pleasure to host Isaac Buo from the Department of Geography of the University of Tartu to show some live coding of satellite data processing with Python and Jupyter notebooks.

Remote sensing data from Earth orbiting satellites have become indispensable in modern geo-spatial sciences. The presentation will cover tasks such as generating Land Surface Temperature (LST) product from satellite imagery from scratch, extraction of information from ready-made products and raster algebra. The main Python libraries used are rasterio, earthpy, pandas, matplotlib and geopandas. The data to be used will be Landsat 8 satellite imagery.
The first part of the workflow focuses on the extraction of intermediate products that are useful for the calculation of LST from satellite imagery. These products are Normalized Difference Vegetation Index (NDVI), Land Surface Emissivity (LSE) and Fractional Vegetation Cover (FVC). These products are not only useful for calculation of LST but are applicable in other remote sensing applications such as vegetation health monitoring and land cover classification.
The second part will cover the pre-processing activity of correcting Landsat 8 thermal bands for the extraction of LST and ultimately generate the LST.
Finally, LST values at certain desired locations will be extracted using geospatial Python libraries and results will be visualized within the Jupyter Notebook.

Looking forward to see you online.