Landsat-derived spatiotemporal variations of land surface temperature

The datasets are supplementary to the article by Gök et al. (2024), in which Landsat-derived land surface temperature (LST) trends of the Swiss Alps are mapped and analyzed. The LST trends were obtained through the regression of a harmonic model, which includes a linear trend component, within Google Earth Engine. These Landsat-derived LST trends are subject to bias due to changes in Landsat acquisition times. The LST trend bias was estimated using modelled incoming shortwave radiation and further calibrated with LST data from high alpine weather stations.

The associated Jupyter notebook (Landsat_LSTtimeseries_gee.ipynb) to reproduce the Landsat LST products requires the Google Earth Engine (GEE) Python API and uses Landsat TM, ETM+, and OLI/TIRS - Surface temperature data.