2026-05-17T12:57:15Zhttp://doidb.wdc-terra.org/oaip/oai

oai:doidb.wdc-terra.org:80182024-11-14T15:11:04ZDOIDBDOIDB.GFZ

false4DOIDB.GFZ 10.5880/GFZ.3.3.2023.005 Gök, Deniz Tobias Deniz Tobias Gök 0009-0005-0225-5087 GFZ German Research Centre for Geosciences Scherler , Dirk Dirk Scherler 0000-0003-3911-2803 GFZ German Research Centre for Geosciences Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany Wulf, Hendrik Hendrik Wulf 0000-0002-6161-428X Remote Sensing Laboratories, University of Zurich; GFZ Data Services 2024 Land Surface Temperature Trends Mean annual land surface temperature Annual LST amplitude Phase EARTH SCIENCE > LAND SURFACE > LAND TEMPERATURE > LAND SURFACE TEMPERATURE Gök, Deniz Tobias Deniz Tobias Gök 0009-0005-0225-5087 GFZ German Research Centre for Geosciences Scherler , Dirk Dirk Scherler 0000-0003-3911-2803 GFZ German Research Centre for Geosciences Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany Gök, Deniz Tobias GFZ German Research Centre for Geosciences Dataset DOI of paper when available 10.5066/P9C7I13B 10.5066/P9OGBGM6 10.5066/P9IAXOVV CC BY 4.0 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.
Swiss Alps 5.57298 10.9695 45.3417 47.9653